Autonomous Driving Target Detection Method Based on Improved YOLOv5
Aiming at the problems of missed targets,inaccurate target localization,insufficient target feature expression,and unsat-isfactory target recognition effects in the current object detection algorithms for autonomous driving in traffic scenarios,a road object de-tection method based on transformer prediction heads-YOLOv5(TPH-YOLOv5)was proposed.Firstly,to reduce the risk of missed ob-jects caused by drastic changes in object scales,a detection head for detecting small objects was added,and a Transformer prediction head was used to capture global information for precise object localization in high-density scenes.Secondly,to enhance the feature ex-pression ability of the model,the output of the convolutional layer was weighted using the selective interactive module with affinity learning(SIM AM)module.Finally,in order to improve the accuracy of target recognition,four spatial pyramid pooling(SPP)blocks were added to the neck of the network for multi-scale fusion,and the Euclidean distance-IOU(EIOU)was used as the bounding box loss function to accelerate convergence speed and improve regression accuracy.Through ablation,comparison,and visualization experi-ments,it is shown that the proposed algorithm improves the average precision by 8.1％compared to YOLOv5,significantly reduces the missed detection rate,and enhances the object detection performance.
object detectionautonomous drivingYOLOv5multi-scale detectionloss function
NETL
NSTL
万方数据
