Research on Small Sample Ship Target Detection Based on SAR Image

This paper studies the small sample target detection based on SAR images. Aiming at the problem of target semantic information loss in small sample targets, the residual network structure is optimized and improved based on YOLOv3 algorithm, and the data enhancement method is used to increase the number of small sample ships in the dataset. In this paper, three different ship detection data sets are processed, and the public datasets of multi-source SAR satellite ground object types are classified according to the different shooting satellites and polarization methods. A total of ten standard VOC datasets suitable for different algorithms are produced. In this paper, the constructed algorithm is compared with three comparison algorithms. The results show that although the detection performance of YOLOv3 is better than that of RCNN series, the detection accuracy of the two for small sample targets needs to be improved. Aiming at the small sample ship target with complex background in the data set, the detection effect of our algorithm is better. The mAP is used to verify the detection accuracy. The results show that the improved algorithm’s mAP is 2% higher than others.