A lightweight and efficient ship detection method based on the YOLO-FNC model is proposed for complex environments such as ports with dense traffic.

First, a FasterNeXt neural network module is designed on the basis of the FasterNet method and replaces the C3 module in the YOLO model to ensure faster operation without affecting accuracy. Second, a normalization-based attention module (NAM) is integrated into the network structure and the sparse weight penalty is used to suppress the feature weights and ensure more efficient weight calculation. Finally, a new bounding box regression loss is proposed to speed up the prediction frame adjustment and increase the regression rate, thereby improving the convergence rate of the network mode.

The experimental results show that when performing detection experiments on ship datasets in a self-built complex environment, the proposed method improves the mAP@0.5 by 6.35%, reduces the parameter count by 9.74% and reduces the computational complexity by 11.39%.

The proposed method effectively achieves lightweight and high-precision ship detection compared with the YOLOv5s algorithm.