FSN-YOLO: Nearshore Vessel Detection via Fusing Receptive-Field Attention and Lightweight Network

Abstract

Vessel detection is critical for ensuring maritime transportation and navigational safety, creating a pressing need for detection methodologies that are more efficient, precise, and intelligent in the maritime domain. Nonetheless, accurately detecting vessels across multiple scales remains challenging due to the diversity in vessel types and locations, similarities in ship hull shapes, and disturbances from complex environmental conditions. To address these issues, we introduce an innovative FSN-YOLO framework that utilizes enhanced YOLOv8 with multi-layer attention feature fusion. Specifically, FSN-YOLO employs the backbone structure of FasterNet, enriching feature representations through super-resolution processing with a lightweight Convolutional Neural Network (CNN), thereby achieving a balance between processing speed and model size without compromising accuracy. Furthermore, FSN-YOLO uses the Receptive-Field Attention (RFA) mechanism to adaptively fine-tune the feature responses between channels, significantly boosting the network’s capacity to capture critical information and, in turn, improve the model’s overall performance and enrich the discriminative feature representation of ships. Experimental validation on the Seaship7000 dataset showed that, compared to the baseline YOLOv8l approach, FSN-YOLO considerably increased accuracy, recall rates, and mAP@0.5:0.95 by absolute margins of 0.82%, 1.54%, and 1.56%, respectively, positioning it at the forefront of current state-of-the-art models.