Anchor Free Object Detection Algorithm Based on Soft Label and Sample Weight Optimization

Abstract

Similar to the Anchor Based object detection algorithm,the Anchor Free object detection algorithm based on feature points also encounters the problem of ambiguous samples when dividing positive and negative samples.That is,the training samples are divided either positive or negative according to the specific threshold and the position of feature points,and when the model trains samples whose feature point is near the borderline,it will incur great loss,which will make the model pay too much attention to these ambiguous samples and reduce the performance of the model.In view of this situation,this paper proposes to improve the Anchor Free object detection algorithm based on feature points from the three aspects of soft label,loss function and weight optimization.By making full use of Center Ness,the impact of ambiguous samples on network performance is mitigated and the accuracy of object detection is improved.To prove the effectiveness of the proposed method,the FCOS object detector is employed in the comparative experiments on the classical Pascal VOC and MS COCO datasets,respectively.Finally,the mAP of the detector on Pascal VOC dataset increases to 82.16%(an increase of 1.31%) and the AP50-95 on MS COCO dataset increases to 35.8% (an increase of 1.3%).

Keywords

• object detection|ambiguous samples|anchor free|center ness|sample weight optimization