Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 1461-1468· 0 citations· 23 references
Abstract
One of the most important features of Advanced Driver Assistance Systems (ADAS) and Intelligent Transportation Systems (ITS), namely Traffic Sign Recognition (TSR), is a computer vision system that can automatically recognize and classify traffic signs to ensure road safety. In the proposed research work, a dataset provided by Kaggle on traffic signs containing 6117 images of various categories of traffic signs such as speed limit, stop, no entry, yield, pedestrian crossing, etc., has been used to propose a framework for classification of traffic signs using Deep Learning (DL) techniques. In the proposed research work, a combination of two architectures, namely VGG16 and DenseNet121, has been used by employing feature level ensemble techniques to improve the efficiency of the developed ensemble architecture by comparing the efficiency of the proposed ensemble architecture with existing architectures. The developed ensemble architecture has been found to attain an accuracy of 99.54%, precision of 99.69%, recall of 99.54%, and F1-score of 99.57%, which shows that the proposed ensemble architecture is productive and reliable compared to existing architectures.
A CNN-based framework designed to accurately detect and classify traffic signs from input images and can be effectively integrated into intelligent transportation systems, driver assistance technologies, and autonomous vehicle applications is presented.
Yalla Lokesh Kumar, T. Ramakrishna· International Journal for Re...· 0 citations
Three models of object detection based on YOLO algorithm, YOLOv5, YOLOv7, and YOLOv8 for detecting Indian traffic signs and potholes are compared to prove that the YOLO models of object detection are of great use in real-time ADAS and show good efficiency in road scene perception.
G. Gayathri, C. Chandrika· International Journal for Re...· 0 citations
A rigorous comparative analysis of deep learning models, specifically U-Net, Mask R-CNN, and YOLOv8-Seg, for real-time traffic sign segmentation is presented, finding that YOLOv8-Seg achieves an optimal trade-off with a mean Average Precision.
Anaxon Muqimova· Techscience uz - Topical Iss...· 0 citations
A new model is proposed, which improves the best performance of existing databases; it merges the Arabic Traffic Signs collection with the Jordanian dataset, and it uses several data augmentation techniques to create the largest publicly available database in the Arabic world to date.
Elena Battini Sonmez, Hamza Arnaout, Albano Younes· International Advanced Resea...· 0 citations
Traffic accidents are one of the most significant societal problems worldwide, resulting in loss of life and property. In recent years, powerful solutions for the automatic classification of traffic accidents have been offered by deep learning-based image processing methods. This study comparatively evaluated four conv...
Eren Dağlı, Y. Taşpınar, M. M. Aydın et al.· Applied Sciences· 0 citations
Automatic classification of military aircraft in satellite imagery is a challenging problem with a high risk of error due to the limited pixel area of targets, variations in image resolution and illumination conditions, background complexity, and strong visual similarity among classes. In this study, a deep learning ap...
