Deep Learning Approach for Detecting and Classifying Traffic Signs and Potholes in Indian Roads

Engineering notes

Key topics: autonomous driving, object detection, adas, perception. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

The Advanced Driver Assistance Systems (ADAS) have a significance in enhancing road safety by identifying the traffic signs and road surface problems in real-time. This research paper compares three models of object detection that are based on YOLO algorithm and are YOLOv5, YOLOv7, and YOLOv8 for detecting Indian traffic signs and potholes. The approach used in this research involved the steps of collecting dataset, annotating the data, processing the data, augmenting the data, and training the model. The results after conducting the experiments show that every model has its own particular functionalities. In case of traffic signs detection, YOLOv5 got the highest precision of 99.3 percent, however, YOLOv8 got the highest recall of 83.3 percent and mAP50 of 88.7 percent, showing the capability of identifying the sign on different road conditions. In case of pothole detection, YOLOv5 gave the best precision of 83.1 percent, but YOLOv7 performed best overall with retrieval of recall of 76.7 percent and mAP of 79.0 percent and mAP range from 50 to 95 of 48.3 percent. The comparison proves that the YOLO models of object detection are of great use in real-time ADAS and show good efficiency in road scene perception.

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