Improved YOLOv5 based electric bicycle helmet detection method in complex scenes
Wearing an electric bicycle helmet is an important guarantee for safe riding,and it is of great significance to ensure the personnel safety by effectively detecting the helmet wearing of drivers and passengers of electric bicycles.Due to the factors of mutual occlusion of objects,complex background interferences,and excessive small size of the helmets(the objects)in the detection,the existing methods fail to meet the requirements of helmet detection in complex scenes,so this paper proposes an improved YOLOv5 based electric bicycle helmet recognition method in complex scenes.A new backbone network structure ML-CSPDarknet53 is proposed to enhance the feature extraction capability of the network.The lightweight up-sampling operator CARAFE is introduced.The semantic information of the feature map is used to expand the receptive field.A coordinate convolution CoordConv module is built to enhance the network′s perception of spatial information,and the WIoU(wise-IoU)v3 is taken as the bounding box loss function to reduce the adverse impact of low-quality samples on model performance.A rich helmet detection dataset is constructed to verify the improved algorithm.The experimental results show that the accuracy,recall rate,mAP@0.5 and mAP@0.5:0.95 of the proposed algorithm is improved by 2.9%,3.0%,3.4%and 2.2%,respectively,in comparison with that of the original algorithm,and the performance of the proposed algorithm is better than that of the other mainstream detection algorithms.Therefore,the proposed algorithm can meet the requirements of helmet detection of drivers and passengers of electric bicycles in complex scenes of road traffic.
helmet detectionimproved YOLOv5complex sceneobject occlusionfeature extractionup-samplingCoordConvloss function
万方数据
维普
