AM YOLO: adaptive multi-scale YOLO for ship instance segmentation

Abstract

Instance segmentation has seen widespread development and significant progress across various fields. However, ship instance segmentation in marine environments faces challenges, including complex sea surface backgrounds, indistinct target features, and large-scale variations, making it incapable of achieving the desirable results. To overcome these challenges, this paper presents an adaptive multi-scale YOLO (AM YOLO) algorithm to improve instance segmentation performance for multi-scale ship targets in marine environments. Initially, the algorithm proposes a multi-grained adaptive feature enhancement module (MAEM) that utilizes grouped weighting and multiple adaptive mechanisms to enhance the extraction of details and improve the accuracy of multi-scale and global information. Subsequently, this study proposes a refine bidirectional feature pyramid network (RBiFPN) structure, which employs a cross-channel attention adaptive mechanism to integrate feature information and contextual details across different scales fully. Experiments on the challenging MS COCO dataset, COCO-boat dataset, and OVSD dataset show that compared to the baseline YOLOv5s, the AM YOLO model increases instance segmentation precision by 4.0%, 1.4%, and 2.3%, respectively. This improvement enhances the model’s generalization capabilities and achieves an optimal balance between accuracy and speed while maintaining real-time performance, thus broadening the model’s applicability in dynamic marine environments