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.