An improved small target traffic sign detection algorithm for YOLOv5s

The recognition of traffic signs is a significant challenge for the development of intelligent transportation systems. The small target characteristics of traffic signs during automobile driving make them difficult to recognize, leading to easily missed detection and other issues. To address small target traffic signs, we have made corresponding improvements based on the YOLOv5s algorithm model. Firstly, we enhance the Mosaic online data augmentation in the YOLOv5s algorithm and remove incomplete samples during the training process by setting up a threshold. Secondly, we introduce a small target detection head containing more shallow features, removing the detection head used for detecting large targets. Within the feature pyramid, multi-scale attention feature fusion replaces the original concat features fusion, reducing conflicts in features fusion at different levels. Finally, considering the sensitivity to location deviation when calculating the loss for small targets, we combine the Normalized Wasserstein Distance loss and the Focal EIOU loss as location loss. Experiment findings on the CCTSDB-2021 dataset reveal that our improvements have the most obvious enhancement effect on small target detection, increasing mAP@0.5 (mean Average Precision) by 3.5%.