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Multi-category vessel detection and classification based on satellite imagery attract a lot of attention due to their significant applications in the military and civilian domains. In this study, we generated a new Artificial-SAR-Vessel dataset based on the combination of the FUSAR-Ship dataset and the SimpleCopyPaste method. We further proposed a novel multi-category vessel detection called CRAS-YOLO which consisted of a convolutional block attention module (CBAM), receptive fields block (RFB), and adaptively spatial feature fusion (ASFF) based on YOLOv5s. The proposed CRAS-YOLO improved the feature pyramid network based on the path aggregation network (PANet), which integrates the RFB feature enhancement module and ASFF feature fusion strategy to obtain richer feature information and realize the adaptive fusion of multi-scale features (RA-PANet). At the same time, a CBAM is added to the backbone to accurately locate the vessel location and improve detection capability. The results confirmed that the proposed CRAS-YOLO model reached a precision, recall rate, and mean average precision (mAP) (0.5) of up to 90.4%, 88.6%, and 92.1% respectively. The proposed model also outperformed previous studies&#x2019; results in another Sar Ship Detection (SSDD) dataset with precision, recall, and mAP scores of up to 97.3%, 95.5%, and 98.7% respectively.

CRAS-YOLO: A Novel Multi-Category Vessel Detection and Classification Model Based On YOLOv5s Algorithm