Marine-YOLO: A high-precision object detection algorithm for complex maritime environments

作者：Chunliang Ruan, Liang Hong, Junjie Gao · 发表于：Applied Ocean Research · 年份：2026 · DOI：10.1016/j.apor.2026.104994 · 被引用次数：4 · 研究领域：Advanced Neural Network Applications、Infrared Target Detection Methodologies、Maritime Navigation and Safety

• Proposed Marine-YOLO, specifically designed for real-time detection of complex maritime targets, achieving a good balance between accuracy and efficiency. • Designed the C3k2-SP module, enhancing feature representation and environmental robustness through the SC-Dual mechanism. • Introduced SAGA attention, combining axial and efficient channel mechanisms to optimize multi-scale target modeling. • AFAE-Head module adds a P2 layer, effectively addressing small-target miss detection under adverse weather conditions. Sea Surface Object Detection is of great significance for intelligent shipping, marine monitoring, and search and rescue. However, in complex sea-surface scenarios, challenges remain, such as severe weather, illumination changes, large variations in object scale, and missed detections of small targets. To address these issues, this paper proposes Marine-YOLO based on YOLOv11 to improve detection accuracy and robustness in complex sea-surface environments. First, Marine-YOLO introduces a C3k2-SP module into the backbone network to enhance feature representation and environmental robustness. Second, a SAGA attention module is added to the neck to strengthen multi-scale modeling capability. Finally, an AFAE-Head module is designed in the detection head, and a P2 layer is incorporated to optimize small-object detection performance. Experimental results on the WSODD dataset show that Marine-YOLO achieves 76.7% and 44.1% on mAP50 and mAP50–95, respectively, representing ...