EGM-YOLOv8: A Lightweight Ship Detection Model with Efficient Feature Fusion and Attention Mechanisms

Accurate and real-time ship detection is crucial for intelligent waterborne transportation systems. However, detecting ships across various scales remains challenging due to category diversity, shape similarity, and complex environmental interference. In this work, we propose EGM-YOLOv8, a lightweight and enhanced model for real-time ship detection. We integrate the Efficient Channel Attention (ECA) module to improve feature extraction and employ a lightweight Generalized Efficient Layer Aggregation Network (GELAN) combined with Path Aggregation Network (PANet) for efficient multi-scale feature fusion. Additionally, we introduce MPDIoU, a minimum-distance-based loss function, to enhance localization accuracy. Compared to YOLOv8, EGM-YOLOv8 reduces the number of parameters by 13.57%, reduces the computational complexity by 11.05%, and improves the recall rate by 1.13%, demonstrating its effectiveness in maritime environments. The model is well-suited for deployment on resource-constrained devices, balancing precision and efficiency for real-time applications.