A High-Accuracy YOLOv8-ResAttNet Framework for Maritime Vessel Detection Using Residual Attention

Abstract

Against the backdrop of constantly upgrading maritime security requirements and dynamic marine environments, satellite based ship detection has become a key technology for national maritime surveillance, resource management, and environmental protection. However, existing methods often struggle to address ongoing challenges, including insufficient sensitivity to small vessels and susceptibility to errors or missed detections in complex ocean backgrounds caused by wave reflections, cloud cover, and lighting changes. To address these limitations, this study proposes YOLOv8 ResAttNet, an enhanced model that integrates residual learning and attention mechanisms into the YOLOv8 framework. The core innovation lies in a custom designed backbone network that combines multi-scale feature aggregation with an improved ICBAM attention module to achieve precise localization of ship targets while suppressing irrelevant background noise. This architecture dynamically recalibrates feature channel weights through residual attention blocks, enhancing the model's ability to distinguish subtle ship features (such as hull contours and superstructures) in different maritime scenarios. Extensive experiments on high-resolution HRSID datasets have demonstrated the superiority of this model: the average accuracy (mAP50) of YOLOv8 ResAttNet is 95.2%, which is 4.9% higher than the original YOLOv8 and over 4% higher than state-of-the-art models such as YOLO SENet and YOLO11. These improvements highlight its robustness in handling scale changes and complex background interference. The research results emphasize the effectiveness of combining residual connectivity with attention driven feature refinement for maritime target detection, especially in small target scenes. This work not only advances the technological frontier of remote sensing image analysis, but also provides a scalable framework for real-world applications such as illegal fishing monitoring, maritime traffic management, and disaster response. Future research directions include extending the model to multimodal satellite data fusion, optimizing the computational efficiency of edge device deployment, and further bridging the gap between theoretical innovation and maritime surveillance systems.