Remote sensing object detection algorithm based on vector cross product label assignment
Object detection in aerial images has received extensive attention in recent years,and the mainstream remote sensing image object detectors divide positive and negative samples by the intersection-over-union(IoU)between the preset anchor box and ground-truth box.In order to solve the problem of duplicate detection and missed detec-tion in remote sensing images with small and dense objects in the label assignment method based on IoU,a remote sensing image object detection method YOLOXR based on vector cross product label assignment is proposed.First-ly,a rough label assignment strategy is proposed,which uses the vector cross product method to determine whether a pixel is in the oriented object or the rotating square box near the center of the object,so as to determine whether it is a candidate positive sample.Secondly,a rotation center measurement approach is provided to limit the influ-ence of low-quality candidate positive samples on label assignment by judging the distance between the pixel point and the center point using vector cross product and then assigning different weights.Finally,optimal transmission assignment(simOTA)is used to select the optimal matching pairs of ground-truth boxes and the sample points,which minimizes the overall cost,and then assigns the appropriate label to the rotating object.In addition,IoU is replaced by computing the Kullback-Leibler divergence(KLD)of the two-dimensional Gaussian distribution of the ground-truth box and the predicted box to overcome the problem that the rotation IoU loss is not differentiable and the Smooth L1 loss is difficult to be used to weigh the parameters of the oriented bounding boxes.Extensive experi-ments on public remote sensing image object detection datasets DOTA,HRSC 2016 and UCAS-AOD show that the proposed method outperforms most of the current oriented object detection algorithms.
万方数据
维普
