Small target detection algorithm for traffic scenes based on improved YOLOv5-s

A traffic scene tiny target detection method based on enhanced YOLOv5-s was presented to address the issue that the properties of small targets in traffic scenes, such as traffic signs and traffic lights, are not readily apparent. Firstly, a feature supplement module (FSM) was designed to supplement the features of the adjacent deep detection layers by further obtaining the shallow details, which effectively improved the detection effect of small targets, and avoided feature redundancy by matrix operation between adjacent layers. Second, in order to reduce feature conflict and improve the effectiveness of the pyramid feature fusion, an effective fusion module (EFM) was created to handle the horizontal shallow feature and the upsampled feature, respectively. Then, the super enhanced intersection over union (SEIOU) loss calculation method was proposed to improve the regression effect and detection accuracy by adding the distance measurement between the main diagonal of the ground truth box and the prediction box. Finally, experiments were carried out on CCTSDB, S2TLD, the Traffic lights dataset and the PASCAL VOC dataset. According to the results, the proposed algorithm’s accuracy has increased by 2.54%, 3.62%, 4.33%, and 2.01%, respectively, and its detection speed has reached 113 frames per second, making it appropriate for detecting jobs in real-world traffic situations.