Object detection algorithm based on adaptive focal CRIoU loss

In the object detection task,there is no correlation between the regression content of the traditional bounding box regression loss function and the evaluation standard IoU(Intersection over Union),and there is some irrationality for the regression attribute of the bounding box,which reduces the detection accuracy and convergence speed. In addition,the sample imbalance also exists in the regression task,and a large number of low-quality samples affect the loss function convergence. In this paper,a novel loss function,termed as CRIoU(Complete Relativity Intersection over Union),is proposed to improve the detection accuracy and convergence speed. Firstly,this work determines the design idea and determines the improved IoU loss function normal form. Secondly,on the basis of IoU loss,the ratio of the perimeter of the rectangle formed by the two center points and the minimum closure area formed by the two frames is introduced as the penalty term for the distance between the center points of the bounding box,and the improved IoU loss is applied to the non- maximum suppression. Then,the side error of the two frames and the side square of the minimum bounding box are introduced as the side penalty term,a novel loss function,termed as CRIoU (Complete Relativity Intersection over Union),is proposed. Finally,on the basis of CRIoU,an adaptive weighting factor is added to weight the regression loss of high-quality samples,and an AF-CRIoU(Adaptive focal CRIoU) is defined. The experimental results show that the detection accuracy of the AF- CRIoU loss function compared with the traditional non IoU series loss is up to 8. 52%,the detection accuracy of the CIoU series loss is up to 2. 69%,and the A-CRIoU NMS(Around CRIoU Non Maximum Suppression) method compared with the original NMS method is up to 0. 14%. In addition,AF-CRIoU loss is applied to the detection of safety helmet,which also achieves good detection results.