Abstract: Marine ship detection plays an important role in the maritime field. Due to the complicated environment and the diversity of ships, existing methods based on convolutional neural network cannot achieve both high accuracy and real-time performance. To solve the problem of ship’s real-time detection in complicated environment, a YOLO-marine model based on YOLOv4 is proposed in which the domain attention mechanism is introduced into backbone. Firstly, the Mosaic method is used to preprocess the ship data. Then the K-Means++ algorithm is used to get initial anchors. The model is implemented on Darknet for training and evaluation with the real ship dataset. The experimental result show that compared with YOLOv4, YOLO-marine improves the mAP of ship detection task by 2.1 percentage points. The model can effectively improve the accuracy of ship detection while ensuring real-time performance. It also gives outstanding results in small and occluded target.

Key words: ship detection, data enhancement, YOLO, K-means+〖KG-*3〗+, attention mechanism