{"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/position-detection-and-direction-prediction","title":"Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network","arxiv_id":"1806.04828","date":"2018-06-13","proceeding":null,"authors":["Xue Yang","Hao Sun","Xian Sun","Menglong Yan","Zhi Guo","Kun fu"],"abstract":"Ship detection is of great importance and full of challenges in the field of\nremote sensing. The complexity of application scenarios, the redundancy of\ndetection region, and the difficulty of dense ship detection are all the main\nobstacles that limit the successful operation of traditional methods in ship\ndetection. In this paper, we propose a brand new detection model based on\nmultitask rotational region convolutional neural network to solve the problems\nabove. This model is mainly consist of five consecutive parts: Dense Feature\nPyramid Network (DFPN), adaptive region of interest (ROI) Align, rotational\nbounding box regression, prow direction prediction and rotational nonmaximum\nsuppression (R-NMS). First of all, the low-level location information and\nhigh-level semantic information are fully utilized through multiscale feature\nnetworks. Then, we design Adaptive ROI Align to obtain high quality proposals\nwhich remain complete spatial and semantic information. Unlike most previous\napproaches, the prediction obtained by our method is the minimum bounding\nrectangle of the object with less redundant regions. Therefore, rotational\nregion detection framework is more suitable to detect the dense object than\ntraditional detection model. Additionally, we can find the berthing and sailing\ndirection of ship through prediction. A detailed evaluation based on SRSS for\nrotation detection shows that our detection method has a competitive\nperformance.","url_abs":"http://arxiv.org/abs/1806.04828v2","url_pdf":"http://arxiv.org/pdf/1806.04828v2.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":"position-detection-and-direction-prediction","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":"position-detection-and-direction-prediction","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":"position-detection-and-direction-prediction","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":null,"task_name":"Position"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1806.04828","atlas_url":"https://app.syntology.ai/?focus=1806.04828","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04828"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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