Ship target detection in SAR image based on improved YOLOv3

Aiming at the problems of insufficient data training and poor robustness when target detection algorithm was directly applied to SAR image ship detection, this paper proposed a ship detection method based on improved yolov3. From the perspective of improved network training strategy, it improved the adaptability of the algorithm to different ship targets and optimized the detection performance of the algorithm.The improvement of this method included two aspects: on the one hand, this paper introduced the distribution method of positive and negative samples of ATSS based on yolov3. This method improved the imbalance between positive and negative samples and improved the quality of training sample selection.On the other hand, this paper designed the anchor boxes’ super parameter optimization method based on the feature layer, which made the anchor boxes more fit the samples distribution of each detection layer data set, so that the training model converged better.The experimental results were carried out on SSDD and SAR-Ship-Dataset respectively. The improved algorithm has improved the detection performance in inshore and offshore scenes, and verified its effectiveness.