FAD-YOLO: enhanced ship detection via multi-scale feature fusion and dilated convolution-based information enhancement

Abstract Ship detection is vital for maritime safety and surveillance. However, traditional methods face challenges such as high false detection rates and difficulty in detecting small objects in complex backgrounds. To address these issues, we propose FAD-YOLO, an innovative model designed for ship object detection. FAD-YOLO adopts a Multi-scale Cross-layer Fusion Strategy that effectively integrates features across different scales, ensuring the capture of rich contextual information and enhancing ship recognition in complex scenarios. Additionally, a Dilated Convolution-enhanced Spatial Pyramid Pooling Fast module is introduced to extract hierarchical feature information, enhancing the accuracy of small object detection. The Enhanced Information Fusion Module further optimizes computation while preserving strong feature extraction capabilities. Compared to YOLOv8-N/S, FAD-YOLO-N/S improves mAP(50:95) by up to 5.8%, demonstrating superior performance in ship detection tasks. FAD-YOLO not only advances ship detection technology but also offers new insights into object detection methodologies in computer vision.