摘要
Abstract
In view of the false detection,missed detection and low precision caused by the mismatch between the bounding box regression loss function and the detection object scale in vehicle detection,four representative bounding box regression loss functions are contrasted based on the YOLOv5s model,and verified on the datasets of UA-DETRA,VisDrone2019 and KITTI.The missed detection rate,false detection rate,precision,recall rate,mAP@0.5,the bounding box loss value of the iterative process and the object detection results are used to analyze and study the applicable scenarios.The results show that the overall performance of CIoU is the worst,SIoU has the best overall performance on the dataset KITTI,with the highest precision of 94.5%,and its missed detection rate is reduced to 1.2%,which is suitable for the detection tasks of the objects with medium scale.Focal-EIoU is far superior to the other loss functions on the data set VisDrone2019.In comparison with CIoU,its recall rate and mAP@0.5 indicators are improved by 1.6%and 1.8%,and its false detection rate is reduced by 6.9%,and its loss value of the iterative process is much lower than the other loss functions,that is,it is suitable for the detection tasks of the objects with small-scale.WIoU has the best overall performance on the dataset UA-DETRA,and its missed detection rate,recall rate and mAP@0.5 are better than those of the other loss functions,which is suitable for the detection tasks of the objects with large-scale.This study provides an important basis for the selection of bounding box regression loss function for object detection tasks.关键词
车辆检测/边界框回归损失函数/目标尺度/YOLOv5s/CIoU/SIoU/Focal-EIoU/WIoUKey words
vehicle inspection/bounding box regression loss function/object scale/YOLOv5s/CIoU/SIoU/Focal-EIoU/WIoU分类
信息技术与安全科学
