Rotating target detection for nearshore SAR ships based on improved YOLOv7

Abstract

To address the problems of complex background of land buildings and islands in near-shore SAR image ship detection, dense ship docking, and thus inaccurate localization and target miss detection, we propose a YOLOv7 near-shore SAR ship rotation target detection model based on the attention mechanism and KLD improvement. Firstly, considering the lack of attention mechanism and remote dependency of YOLOv7, CA attention mechanism is added to the backbone network to improve the model context encoding capability and enhance the model accuracy. Secondly, the 3D nonreference attention mechanism SimAm is introduced to further improve the attention to ship features. Finally, the angular information is considered for the problem that the ship targets of SAR images are closely aligned in any direction. KLD is used as the localization loss function. The experimental results on the SSDD dataset show that the improved algorithm in this paper improves AP by 14.34% in near-shore scenes and the same in offshore scenes, with 2.22% improvement in all scenes relative to the original YOLOv7 model. The experimental results show that the algorithm applies to detecting ship targets in any direction in the near-shore scenes.