Deep Learning-Based Framework For Robust Traffic Sign Detection Under Challenging Weather Conditions

Abstract

Thanks to the rapid development of computer vision and deep learning technologies, advanced driver assistance systems (ADAS) have recently become widespread. These systems aim to increase driving safety and reduce the number of traffic accidents. Modern cars usually have ADAS systems integrated into their electronics, but other vehicles do not have such an integrated system. This paper presents a portable and image-based ADAS system for real-time detection of traffic signs, vehicles, and pedestrians. To realize real-time detection, the developed system uses the YOLO v5 algorithm. This single-stage detector is very popular as it has high detection speed and accuracy. The model was trained on the study-specific datasets to analyze the developed system. Then, the implementation metrics were calculated to evaluate the training and testing performances of the model. In addition, the model was compared in low-power, high-performance embedded platforms and in a computer to measure the real-time performance. Considering the excellent accuracy and high speed, this study will guide researchers in demonstrating the efficiency and suitability of real-time road object detection with YOLO v5 on mobile platforms.