Research on improved pedestrian and vehicle detection algorithm based on YOLOv5s
Aiming at the problem of high complexity of urban traffic environment and low accuracy of pedestrian and vehicle de-tection results.An improved YOLOv5s pedestrian and vehicle detection algorithm is proposed.Firstly,the SK attention mechanism is added to YOLOv5s,and the GSConv module is selected to replace some convolutional modules in the network,which is used to effec-tively improve the detection accuracy while keeping the network parameters basically unchanged.Secondly,the ECIOU loss function is introduced,which can accelerate the model convergence.Finally,the KITTI dataset is selected to test the effect of the improved algo-rithm.The final experimental results show that the improved YOLOv5s algorithm can improve the average detection accuracy of pedes-trians and vehicles from 81.2%to 87.3%while ensuring that the number of parameters of the algorithm is basically unchanged,which verifies the effectiveness of this study.
YOLOv5sPedestrian and vehicle detectionUrban transportationOptimization of loss function
国家科技期刊平台
NSTL
万方数据
维普
