{"id":3903,"date":"2025-12-15T11:50:28","date_gmt":"2025-12-15T11:50:28","guid":{"rendered":"https:\/\/jita-au.com\/?p=3903"},"modified":"2025-12-15T14:21:18","modified_gmt":"2025-12-15T14:21:18","slug":"cnn-based-road-sign-recognition-for-driver-assistance","status":"publish","type":"post","link":"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/","title":{"rendered":"CNN-Based Road Sign Recognition for Driver Assistance"},"content":{"rendered":"\t\t<div data-elementor-type=\"wp-post\" data-elementor-id=\"3903\" class=\"elementor elementor-3903\" data-elementor-settings=\"{&quot;ha_cmc_init_switcher&quot;:&quot;no&quot;}\">\n\t\t\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-e232701 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-mobile elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"e232701\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-cb97594\" data-id=\"cb97594\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-a18c791 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"a18c791\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 2 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b2fff26 elementor-widget elementor-widget-heading\" data-id=\"b2fff26\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Boris Borov\u010danin, Samed Juki\u0107<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-52044bb elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"52044bb\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-1c82606 elementor-widget elementor-widget-heading\" data-id=\"1c82606\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">CNN-Based Road Sign Recognition for Driver Assistance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-c297c9e elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"c297c9e\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b6ebd13 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"b6ebd13\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<br>DOI: https:\/\/doi.org\/10.7251\/JIT2502093B<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-2f3b63f elementor-align-center elementor-widget elementor-widget-button\" data-id=\"2f3b63f\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/12\/Pages-from-JITA_Vol-15_Issue-2-WEB-2.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb7c4cb elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"cb7c4cb\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-61aebe3 elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"61aebe3\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the\neffectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques\nfor comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as\nresizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes,\ndemonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved\nclassification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance\nmetric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has\nbeen highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field\nof road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the\ncrucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and\nweather conditions are the important factors that should be brought into focus.\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5c5f19e elementor-widget elementor-widget-heading\" data-id=\"5c5f19e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: road sign recognition, machine learning, convolutional neural networks, adas.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4df6678 elementor-widget elementor-widget-heading\" data-id=\"4df6678\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 6.10.2025.<br><b>Paper accepted:<\/b> 24.11.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-6dd494a wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"6dd494a\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-cb049d1 elementor-widget elementor-widget-shortcode\" data-id=\"cb049d1\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3903 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">543<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-20a3f75 elementor-widget elementor-widget-shortcode\" data-id=\"20a3f75\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-511d0df elementor-section-height-min-height elementor-section-full_width elementor-hidden-mobile elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"511d0df\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-0acee9c\" data-id=\"0acee9c\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-c0e98b3 elementor-widget__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"c0e98b3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 2 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b524c19 elementor-widget elementor-widget-heading\" data-id=\"b524c19\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Boris Borov\u010danin, Samed Juki\u0107<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-68f1849 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"68f1849\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-34d6608 elementor-widget elementor-widget-heading\" data-id=\"34d6608\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">CNN-Based Road Sign Recognition for Driver Assistance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-7b2f501 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"7b2f501\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5027126 elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"5027126\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-4a5312c elementor-widget__width-initial elementor-widget elementor-widget-heading\" data-id=\"4a5312c\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2502093B<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-bd86c52 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"bd86c52\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/12\/Pages-from-JITA_Vol-15_Issue-2-WEB-2.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-580ffd3 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"580ffd3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-211717e elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"211717e\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-62b72c3 elementor-widget elementor-widget-heading\" data-id=\"62b72c3\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: road sign recognition, machine learning, convolutional neural networks, adas.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-a2636dd elementor-widget elementor-widget-heading\" data-id=\"a2636dd\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 6.10.2025.<br><b>Paper accepted:<\/b> 24.11.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f4dcd16 wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"f4dcd16\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0f73093 elementor-widget elementor-widget-shortcode\" data-id=\"0f73093\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3903 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">543<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-975233a elementor-widget elementor-widget-shortcode\" data-id=\"975233a\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<section class=\"elementor-section elementor-top-section elementor-element elementor-element-2d980b8 elementor-section-height-min-height elementor-section-full_width elementor-hidden-tablet elementor-hidden-desktop elementor-hidden-laptop elementor-section-height-default elementor-section-items-middle wpr-particle-no wpr-jarallax-no wpr-parallax-no wpr-sticky-section-no\" data-id=\"2d980b8\" data-element_type=\"section\" data-settings=\"{&quot;background_background&quot;:&quot;classic&quot;,&quot;_ha_eqh_enable&quot;:false}\">\n\t\t\t\t\t\t<div class=\"elementor-container elementor-column-gap-default\">\n\t\t\t\t\t<div class=\"elementor-column elementor-col-100 elementor-top-column elementor-element elementor-element-d772afa\" data-id=\"d772afa\" data-element_type=\"column\">\n\t\t\t<div class=\"elementor-widget-wrap elementor-element-populated\">\n\t\t\t\t\t\t<div class=\"elementor-element elementor-element-5597455 elementor-widget__width-inherit elementor-widget-mobile__width-inherit ha-has-bg-overlay elementor-widget elementor-widget-heading\" data-id=\"5597455\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Vol. 15 No. 2 (2025): JITA - APEIRON<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-d995814 elementor-widget elementor-widget-heading\" data-id=\"d995814\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Boris Borov\u010danin, Samed Juki\u0107<b><\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b0a72e2 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"b0a72e2\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-69ba19e elementor-widget elementor-widget-heading\" data-id=\"69ba19e\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">CNN-Based Road Sign Recognition for Driver Assistance<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-96936f4 elementor-widget-divider--view-line elementor-widget elementor-widget-divider\" data-id=\"96936f4\" data-element_type=\"widget\" data-widget_type=\"divider.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-divider\">\n\t\t\t<span class=\"elementor-divider-separator\">\n\t\t\t\t\t\t<\/span>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f04febc elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"f04febc\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Review paper<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-389228b elementor-widget__width-initial elementor-widget-mobile__width-inherit elementor-widget elementor-widget-heading\" data-id=\"389228b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">DOI: https:\/\/doi.org\/10.7251\/JIT2502093B<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-eb8f1c3 elementor-align-center elementor-widget elementor-widget-button\" data-id=\"eb8f1c3\" data-element_type=\"widget\" data-widget_type=\"button.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<div class=\"elementor-button-wrapper\">\n\t\t\t\t\t<a class=\"elementor-button elementor-button-link elementor-size-sm\" href=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/12\/Pages-from-JITA_Vol-15_Issue-2-WEB-2.pdf\">\n\t\t\t\t\t\t<span class=\"elementor-button-content-wrapper\">\n\t\t\t\t\t\t\t\t\t<span class=\"elementor-button-text\">Download Article PDF<\/span>\n\t\t\t\t\t<\/span>\n\t\t\t\t\t<\/a>\n\t\t\t\t<\/div>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-80faf79 elementor-widget__width-inherit elementor-widget elementor-widget-heading\" data-id=\"80faf79\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Abstract<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8cefffd elementor-widget__width-inherit elementor-widget elementor-widget-text-editor\" data-id=\"8cefffd\" data-element_type=\"widget\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t<p>Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus.<\/p>\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-0c5e44b elementor-widget elementor-widget-heading\" data-id=\"0c5e44b\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">Keywords: road sign recognition, machine learning, convolutional neural networks, adas.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-8fcf84a elementor-widget elementor-widget-heading\" data-id=\"8fcf84a\" data-element_type=\"widget\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\"><b>Paper received:<\/b> 6.10.2025.<br><b>Paper accepted:<\/b> 24.11.2025.<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-dee52ab wpr-logo-position-center elementor-widget elementor-widget-wpr-logo\" data-id=\"dee52ab\" data-element_type=\"widget\" data-widget_type=\"wpr-logo.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\n\t\t\t<div class=\"wpr-logo elementor-clearfix\">\n\n\t\t\t\t\t\t\t\t<picture class=\"wpr-logo-image\">\n\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t<img decoding=\"async\" src=\"https:\/\/jita-au.com\/wp-content\/uploads\/2024\/03\/cc-by-1.png\" alt=\"\">\n\n\t\t\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\t\t\t\t\t<\/picture>\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\t\t<a class=\"wpr-logo-url\" rel=\"home\" aria-label=\"\" href=\"https:\/\/jita-au.com\/\"><\/a>\n\t\t\t\t\n\t\t\t<\/div>\n\t\t\t\t\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-5cb9871 elementor-widget elementor-widget-shortcode\" data-id=\"5cb9871\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\"><div class=\"post-views content-post post-3903 entry-meta load-static\">\r\n\t\t\t\t<span class=\"post-views-icon dashicons dashicons-chart-bar\"><\/span> <span class=\"post-views-label\">Post Views:<\/span> <span class=\"post-views-count\">543<\/span>\r\n\t\t\t<\/div><\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-87a5ee0 elementor-widget elementor-widget-shortcode\" data-id=\"87a5ee0\" data-element_type=\"widget\" data-widget_type=\"shortcode.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-shortcode\">Downloaded Article PDF: <span class=\"snr-download-count-num\" data-post-id=\"0\">0<\/span> times\n<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t<\/section>\n\t\t\t\t<\/div>\n\t\t","protected":false},"excerpt":{"rendered":"<p>Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paperDOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025.<\/p>\n","protected":false},"author":1,"featured_media":3827,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"elementor_header_footer","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3903","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>CNN-Based Road Sign Recognition for Driver Assistance - JITA -Journal of Information Technology and Application<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"CNN-Based Road Sign Recognition for Driver Assistance - JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"og:description\" content=\"Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paperDOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\" \/>\n<meta property=\"og:site_name\" content=\"JITA -Journal of Information Technology and Application\" \/>\n<meta property=\"article:published_time\" content=\"2025-12-15T11:50:28+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-12-15T14:21:18+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/12\/Pages-from-JITA_Vol-15_Issue-2-WEB.jpg\" \/>\n\t<meta property=\"og:image:width\" content=\"612\" \/>\n\t<meta property=\"og:image:height\" content=\"805\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/jpeg\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"4 minutes\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"Article\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/#article\",\"isPartOf\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\"},\"author\":{\"name\":\"admin\",\"@id\":\"https:\/\/jita-au.com\/#\/schema\/person\/d4becda53cfcbc99c449927eabf3877f\"},\"headline\":\"CNN-Based Road Sign Recognition for Driver Assistance\",\"datePublished\":\"2025-12-15T11:50:28+00:00\",\"dateModified\":\"2025-12-15T14:21:18+00:00\",\"mainEntityOfPage\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\"},\"wordCount\":738,\"publisher\":{\"@id\":\"https:\/\/jita-au.com\/#organization\"},\"image\":{\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/jita-au.com\/wp-content\/uploads\/2025\/12\/Pages-from-JITA_Vol-15_Issue-2-WEB.jpg\",\"inLanguage\":\"en-US\"},{\"@type\":\"WebPage\",\"@id\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\",\"url\":\"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/\",\"name\":\"CNN-Based Road Sign Recognition for Driver Assistance - 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JITA -Journal of Information Technology and Application","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/jita-au.com\/index.php\/2025\/12\/15\/cnn-based-road-sign-recognition-for-driver-assistance\/","og_locale":"en_US","og_type":"article","og_title":"CNN-Based Road Sign Recognition for Driver Assistance - JITA -Journal of Information Technology and Application","og_description":"Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paperDOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. Paper received: 6.10.2025.Paper accepted: 24.11.2025. Vol. 15 No. 2 (2025): JITA &#8211; APEIRON Boris Borov\u010danin, Samed Juki\u0107 CNN-Based Road Sign Recognition for Driver Assistance Review paper DOI: https:\/\/doi.org\/10.7251\/JIT2502093B Download Article PDF Abstract Considering established relevance to the GTSRB dataset, it is important to emphasize that research investigates the effectiveness of convolutional neural networks (CNN) in the field of road sign recognition. Following that wide range of techniques for comprehensive preprocessing pipelines were implemented, including data normalization and augmentation as well as resizing images. The CNN model has demonstrated the ability to overcome adverse conditions across multiple road sign classes, demonstrating outstanding scores against the performance metrics used in testing and evaluation process. Model achieved classification accuracies exceeding 99% across most categories. Nevertheless, in certain classes there is presence of performance metric decline related to the inaccurate visualization and contradiction of features. The crucial role of the preprocessing phase has been highlighted while the implementation of the CNN model has been identified as one of the most reliable approaches in the field of road sign recognition. However future implications must be considered to achieve the full potential of the model. Some of the crucial contributions for the future will be introducing real life variation in the dataset. On the other hand, occlusion, lighting and weather conditions are the important factors that should be brought into focus. Keywords: road sign recognition, machine learning, convolutional neural networks, adas. 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