Improved YOLOv3 Remote Sensing Target Detection Based on Improved Dense Connection and Distributional Ranking Loss

Abstract

Aiming at solving the problems of small object size,uneven sample distribution,and unclear features in remote sensing images,an improved YOLOv3 object detection algorithm is proposed.The Stitcher data enhancement method is used to solve the problem of uneven distribution of small object samples.The VOVDarkNet-53 is proposed.The residual modules of the fourth downsampling in DarkNet-53 are reduced from eight to four.And then the dense connection mode of VOVNet is adopted to extract lower features of small objects to increase the network receptive field.The distributional ranking loss is used to improve the classification loss in YOLOv3 to solve the problem of imbalance between positive and negative samples in single-stage object detector.Comparative experiments are carried out on HRRSD remote sensing datasets by using YOLOv3 object detection algorithm and improved YOLOv3 algorithm.The results demonstrate that the proposed algorithm can achieve better performance of higher detection accuracy of the improved YOLOv3 algorithm for small objects and medium objects are improved by 7.2% and 2.1%,respectively.Although the detection accuracy for large objects is reduced by 1%,the average detection accuracy (mAP) is improved by 4.1%,and the recall and accuracy are also improved.

Keywords

• remote sensing image|object detection|yolov3|baseline|sample imbalance