Maritime Ship Detection Method for Satellite Images Based on Multiscale Feature Fusion

Remote sensing ship recognition technology is one of the important research areas for achieving ocean security monitoring. However, maritime ship targets features such as multiscale, arbitrary direction, and dense arrangement, and their imaging is often influenced by factors such as sea fog and sea surface background. It is challenging for fast and accurate detection of remote sensing ships. We propose a new ship detection algorithm YOLO-remote sensing ship detection (YOLO-RSSD) based on YOLOv5, which effectively improves the accuracy of ship detection while ensuring detection speed. It is attributed to our proposed four optimization measures in this model. First, the K-means++ algorithm is introduced in the data preprocessing part to improve the clustering effect and obtain candidate box sizes suitable for multiscale ship dataset. Then, an improved bidirectional feature pyramid network structure is embedded in the feature fusion part to achieve cross-layer multiscale weighted feature fusion. Third, the original bounding box regression loss function is replaced with the EIoU loss, which is effective for accelerating the convergence and improving the regression accuracy of predicted boxes. Finally, a channel attention mechanism is introduced in the convolutional unit to enhance the model's ability to capture ship features. Experimental results show that the YOLO-RSSD model achieves a detection accuracy of 96.1% for remote sensing ships, which is 4.3% higher than the original YOLOv5 network. In addition, YOLO-RSSD performs good robustness and generalization ability. This means that our method has high practical value and provides a new solution for the analysis and application of remote sensing images.