{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-ship-detection-of-remote-sensing","title":"Automatic Ship Detection of Remote Sensing Images from Google Earth in Complex Scenes Based on Multi-Scale Rotation Dense Feature Pyramid Networks","arxiv_id":"1806.04331","date":"2018-06-12","proceeding":null,"authors":["Xue Yang","Hao Sun","Kun fu","Jirui Yang","Xian Sun","Menglong Yan","Zhi Guo"],"abstract":"Ship detection has been playing a significant role in the field of remote\nsensing for a long time but it is still full of challenges. The main\nlimitations of traditional ship detection methods usually lie in the complexity\nof application scenarios, the difficulty of intensive object detection and the\nredundancy of detection region. In order to solve such problems above, we\npropose a framework called Rotation Dense Feature Pyramid Networks (R-DFPN)\nwhich can effectively detect ship in different scenes including ocean and port.\nSpecifically, we put forward the Dense Feature Pyramid Network (DFPN), which is\naimed at solving the problem resulted from the narrow width of the ship.\nCompared with previous multi-scale detectors such as Feature Pyramid Network\n(FPN), DFPN builds the high-level semantic feature-maps for all scales by means\nof dense connections, through which enhances the feature propagation and\nencourages the feature reuse. Additionally, in the case of ship rotation and\ndense arrangement, we design a rotation anchor strategy to predict the minimum\ncircumscribed rectangle of the object so as to reduce the redundant detection\nregion and improve the recall. Furthermore, we also propose multi-scale ROI\nAlign for the purpose of maintaining the completeness of semantic and spatial\ninformation. Experiments based on remote sensing images from Google Earth for\nship detection show that our detection method based on R-DFPN representation\nhas a state-of-the-art performance.","url_abs":"http://arxiv.org/abs/1806.04331v1","url_pdf":"http://arxiv.org/pdf/1806.04331v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-ship-detection-of-remote-sensing","repo_url":"https://github.com/AbdulrahmanCE/-pneumonia-detection-","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"automatic-ship-detection-of-remote-sensing","repo_url":"https://github.com/DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"automatic-ship-detection-of-remote-sensing","repo_url":"https://github.com/DetectionTeamUCAS/RRPN_Faster-RCNN_Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"automatic-ship-detection-of-remote-sensing","repo_url":"https://github.com/DetectionTeamUCAS/RRPN_Faster_RCNN_Tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.04331","atlas_url":"https://app.syntology.ai/?focus=1806.04331","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}