Aiming at the challenges of significant scale and perspective variations of targets, marine environmental variability, and dataset scarcity in ship detection under drone vision, this paper proposes the EPA-YOLOv10 ship detection model to effectively suppress wave interference and enhance small target detection accuracy. At the algorithmic level: (1) the C2f-EMSC multi-scale feature enhancement module is designed to achieve local and global feature fusion of ships and multi-scale integration of distant and nearby ship targets; (2)the PTSSA spatiotemporal attention mechanism is proposed to suppress sea surface interference through reverse spatial attention. Additionally, to address the scarcity of ship data, a multi-source ship dataset containing four major ship categories is constructed. Experiments show that the detection accuracy of the proposed model on the self-built dataset Myship is significantly improved with mAP0.5.
Anti-Interference and Multi-Scale Ship Detection Algorithm Based on YOLOv10
2025-05-23
633029 byte
Conference paper
Electronic Resource
English