CR-YOLOv8: Multiscale Object Detection in Traffic Sign Images

Abstract

Due to the large-scale changes of different forms of traffic signs and the rapid speed of vehicles, the detection accuracy and real-time performance of general object detectors are greatly challenged, especially the detection accuracy of small objects. In order to solve this problem, a multi-scale traffic sign detection model CR-YOLOv8 is proposed based on the latest YOLOv8. In the feature extraction stage, the attention module is introduced to enhance the channel and spatial features, so that the network can learn the key information of the small objects more easily. The RFB module is introduced in the feature fusion stage, which improves the feature diversity with less computational overhead and improves the network's ability to detect multi-scale objects. By improving the loss function to enable the model to effectively balance multi-scale objectives during training, the model generalization ability is improved.The experimental results on TT100k dataset show that compared with the baseline network, the average detection accuracy of the improved method is increased by 2.3 %, and the detection accuracy of small objects is increased by 1.6 %, which effectively reduces the detection accuracy gap among different scales.