Abstract: Under the influence of natural factors such as bad weather and waves,it is often difficult to effectively carry out ship target monitoring based on visible light data and other means,which requires the use of active microwave imaging satellite synthetic-aperture radar (SAR) for image interpretation.To address the issue of inaccurate feature extraction by deep learning when dealing with small datasets and images,as well as the problem of high data similarity,a cross-scale fusion mechanism based on YOLOv5-ResNet is proposed to redefine the loss function.The research shows that there is a certain improvement in the accuracy of identifying SAR ship targets:the maximum accuracy of identifying single ships is 93%,which is 4% higher than YOLOv5 and 20% higher than YOLOv5-ResNet50.In near-shore ship target detection,it effectively reduces the unnecessary increase in error rate caused by poor data set quality and inappropriate model training methods.

Key words: SAR images, Space-borne SAR images, Ship target detection, YOLOv5, ResNet, Cross scale fusion