Performance analysis of the Yolov5 model for traffic sign detection / Nur Izzati Anaz Anizan ... [et al.] - Anaz Anizan, Nur Izzati; Ahmat Ruslan, Fazlina; Abd Razak, Noorfadzli; Abdul Aziz, Mohd Azri; Johari, Juliana (UiTM Press, 2024)
This study evaluates the performance of the YOLOv5 model in the detection of traffic signs under a diverse range of environmental conditions, assessing its performance through a comprehensive set of experiments. This study assesses the model's precision in identifying signage categories across a variety of lighting conditions and perspectives by employing a robust dataset that includes 1,596 images of a wide range of traffic signs. The model's ability to maintain high detection accuracy in optimal conditions is the primary focus of the analysis, which also emphasizes the challenges encountered in adverse lighting conditions such as direct sunlight and low-light settings in parking lots. The results indicate that YOLOv5 is highly reliable in unobstructed and clear conditions, but its reliability decreases in complex environments. This paper examines potential enhancements and future research directions, such as exploring of alternative model architectures and the implementation of advanced data augmentation techniques, to improve the adaptability and robustness of traffic sign detection systems.