Abstract
Ship detection in SAR images is a challenging problem. CNN-based ship detection method in SAR images has achieved remarkable results. Due to the multi scale of the ships and interference from complex sea conditions or nearshore background in SAR images, many false alarms and missed detections can occur in ship detection. To solve these problems, a multi-scale ship detection network in SAR images based on attention and weighted fusion is proposed in this paper. First, a higher-resolution detect head is added based on the YOLOv5 framework for detecting tiny-scale ships in SAR images. Then, the coordinate attention block is introduced to refine the location features of ship targets and suppress the interference of complex background. Finally, in the feature fusion stage, adaptive weighted feature fusion is used to reduce feature redundancy. Experiments on the SSDD dataset show the effectiveness of the proposed method.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
