Seatbelt Behavior Detection of Vehicle Occupants Based on Improved YOLOv5s
The detection of seatbelt wearing behavior of vehicle-borne personnel plays an important role in ensuring human life safety.Aiming at the low detection accuracy of seatbelt worn by vehicle occupants in complex environments,an improved detection method based on YOLOv5s is proposed.The detection method takes YOLOv5s as the basic network and improves on it.In order to improve the ability of the depth model to extract feature information,the receptive field of the network is expanded by using the re-ceptive field RFB module,and the hybrid receptive field is obtained by using the multi-branch structure of the RFB module.Adding the efficient channel attention(ECA)modules to make the entire network more focused on extracting the feature information.The loss function of the original YOLOv5s is replaced by the EIOU to further improve the detection accuracy of the safety belt.The exper-imental results show that compared with the original YOLOv5s network,the mean average precision(mAP)of the improved network is increased by 2.2％,and the precision by 5.1％.The improved network has a good enhancement effect,which shows the effective-ness of the method.
seatbeltYOLOv5sreceptive fieldRFB moduleattention mechanismloss function
万方数据
维普
