The use of remote sensing images to monitor ships on the sea has become a hot spot in current research. In order to solve the defects of traditional ship detection that requires manual feature selection, time-consuming and labor-consuming, and the original YOLO algorithm has low detection accuracy for densely distributed small targets. This paper proposes a remote sensing image ship detection method based on YOLOv5, using the remote sensing data set provided by the Kaggle platform, training on the Pytorch framework, the loss function is designed as CIOU_LOSS, and the selection of the target frame uses the DIOU_NMS algorithm to make the occluded and overlapped The target detection effect is enhanced. After experimental comparison, the detection accuracy of this target detection model for occluded and densely arranged ships is better than other models, and its average detection accuracy is increased from the original 88.75% to 91.27%.