Research and Implementation of an Embedded Traffic Sign Detection Model Using Improved YOLOV5

This study proposes an embedded traffic sign detection system, YOLOV5-MCBS, based on an enhanced YOLOv5 algorithm. This system aims to mitigate the impact of traditional target detection algorithms’ high computational complexity and low detection accuracy on traffic sign detection performance, thereby improving accuracy and real-time performance. Our primary objective is to develop a lightweight network that enhances detection accuracy, enabling real-time detection on embedded systems. First, to minimize computation and model size, we replaced the original YOLOv5 algorithm’s backbone feature network with a lightweight MobileNetV3 network. Subsequently, we introduced the convolutional block attention module into the neck network to optimize the feature fusion stage’s attention and enhance model detection accuracy. Concurrently, we employed the bidirectional feature pyramid network in the neck layer for multi-scale feature fusion. Additionally, we incorporated a small target detection layer into the original network output layer to enhance detection performance. What’s more, we transplanted the enhanced algorithm into a Raspberry Pi embedded system to validate its real-time detection performance. Finally, we conducted computer simulations to assess our algorithm’s performance by comparing it with existing target detection algorithms. Experimental results suggest that the enhanced algorithm achieves an average precision mean (mAP @ 0.5) value of 95.3% and frames per second value of 91.1 on the embedded system.