Abstract

To address critical challenges in maritime ship detection within complex surveillance imagery, including severe background interference, extreme scale variation, and fine-grained category confusion, this study proposes Maritime Scene Collaborative You Only Look Once (MSC-YOLO), an improved detection model for fixed-location maritime surveillance scenarios. First, a Maritime Scene Adaptive Attention Module (MSAM) is introduced to suppress water-surface clutter and enhance structurally informative ship responses through bidirectional feature regulation, thereby strengthening feature representation in background-complex scenes. In addition, a Scale-aware Dynamic Head (SDA-Head) is designed by integrating deformable convolution with parallel scale-aware prediction branches to improve detection coverage for vessels under pronounced scale variation. Furthermore, a Class Prototype Guided (CPG) module is developed, incorporating class-level prototypes and category-similarity priors to improve the discriminative representation of visually similar ship categories and component states. Experimental results on the constructed maritime surveillance dataset show that MSC-YOLO achieves 0.9723 mAP@50, 0.7315 mAP@50–95, 0.8903 Precision, and 0.9883 Recall. Compared with YOLOv11n, the proposed model improves mAP@50 by 17.77%, Precision by 21.82%, and Recall by 8.16%, indicating clear advantages in target discovery, clutter robustness, and difficult-target coverage in complex maritime surveillance scenes. Visualization and confusion-matrix analyses further show reduced background interference and stronger class-wise discrimination. Overall, MSC-YOLO demonstrates effective and reliable performance for complex maritime surveillance scenarios.

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