Shape-adaptive ellipse label assignment for remote sensing image based on FCOS
Anchor-free object detection algorithms have experienced rapid development in object detection in recent years.However,in remote sensing images,the objects with arbitrary angles,dense distribution,and large shape differences make the detection still a challenge.Therefore,an anchor-free method based on improved fully convolu-tional one-stage(FCOS)is proposed.Firstly,to mine more potential high-quality anchor points,a shape-adaptive feature point sampling method based on the ellipse equation is proposed.To further reduce the negative influence of low-quality anchor points,the ellipse centerness is proposed.It can provide more accurate and reasonable weights than the traditional centerness.In addition,to address the inconsistency between classification and regression,a joint intersection over union(IoU)guidance strategy is proposed.The proposed ellipse centerness and IoU score are combined as quality scores to guide the training of the classification branch and to make the results of regression more accurate.The mean average precision on the DOTA 1.0 dataset reaches 79.17％,which is better than most existing anchor-free detection methods.
万方数据
维普
