Ship detection based on YOLO algorithm for visible images

Ship detection is a crucial task for waterway surveillance and channel optimization, especially in close proximity to the shore. However, detecting ship in visible image-based detection remains a challenge due to the limited nature of visible image datasets. To address this issue, the Inland Ships Data Set (ISDS) is constructed to facilitate research on ship identification. On the other hand, most detection methods struggle to accurately identify ships that are small in size. Therefore, a visible image-based ship detection model is proposed that employs a multi-scale weighted feature fusion structure with the YOLOv4 detection model to improve the efficacy of small ship detection. Specifically, the YOLOv4 model is improved through fusing multi-scale feature, redesigning priori frame, and enhancing loss function. The model, named YOLOv4-MSW (i.e. YOLOv4 based on Multi-Scale Weighted feature fusion), exhibits improved performance on ship detection in experiments conducted on the ISDS dataset, outperforming the original YOLOv4 model by improving the average precision (AP) by 4.87% and the recall rate by 10.03%. Meanwhile, the model achieve better detection accuracy and improve the average precision rate by at least 0.86% compared to existing learned object detection methods. The code related to this work are released at . The whole dataset is available at . We propose a visible image-based ship detection model that employs a multi- scale weighted feature fusion structure with the YOLOv4 detection model to improve the efficacy of small ship detection.image