A novel lightweight real-time traffic sign detection method based on an embedded device and YOLOv8

Traffic sign recognition, as one of the key steps of intelligent driving technologies, effectively avoids most traffic accidents by detecting the location and type of traffic signs in real time and providing the information to drivers or autonomous vehicles promptly. In addition, edge devices close to users has become an inevitable requirement for the development of IoT technology, real-time computing and the realization of network edge intelligence. Nowadays, the YOLO algorithm in object detection, developed to YOLOv8, is accompanied by various lightweight networks and lightweight methods to win by “fast”, so this paper will propose an algorithm, fusing Ghost module and Efficient Multi-Scale Attention Module into YOLOv8, so that the model can improve the computing speed while maintaining the original characteristics. Furthermore, we choose Raspberry Pi as the object detection device due to its many characteristics such as lightweight, low power consumption. Through experiments, the model is trained on CCTSDB dataset, and the improved algorithm is tested on Raspberry Pi 4B. The results show that for three types of traffic signs, namely prohibited, indication and warning, the recognition accuracy mAP reaches 93.5% on the poor weather test set and 82.9% on the original test set, and the inference delay of Raspberry Pi reaches 0.79 s, which is effective in actual road test experiments. The improved model’s accuracy has increased by 6.4% and 3.8% on two separate test sets compared to the original model, while the detection time has been reduced by 0.12 s. This research is of great significance to the maturation of assisted driving and autonomous driving technologies.