Research on vehicle target detection in blizzard scene based on improved YOLOv5s

In the field of autonomous driving, object detection is the core foundation of autonomous driving environment perception, and it is also the premise for the safe operation of the whole system, and the accuracy of object detection in bad weather is also a hot research content in the entire industry. To this end, this paper proposes an improved algorithm for YOLOv5s in Blizzard scenarios. (1) The C3 module in the YOLOv5s backbone network is replaced with a C2f module with a richer gradient flow to improve the detection accuracy of small targets. (2)The attention mechanism SE was introduced to adjust the channel weight, which strengthened the extraction of important features of the vehicle. (3) The loss function C_IOU is replaced with E_IOU, which accelerates the convergence of the prediction frame and improves the regression accuracy. And the data set constructed in this paper is verified, and the results show that: Compared with YOLOv5s, the accuracy of the algorithm proposed in this paper is increased by 6.7% to 91.3%. The recall rate increased by 2% to 72.2%; The [email protected] value increased by 3.5% to 81.9%,The detection performance of the model in Blizzard scenarios is effectively improved.