{"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/object-detection-in-equirectangular-panorama","title":"Object Detection in Equirectangular Panorama","arxiv_id":"1805.08009","date":"2018-05-21","proceeding":null,"authors":["Wenyan Yang","Yanlin Qian","Francesco Cricri","Lixin Fan","Joni-Kristian Kamarainen"],"abstract":"We introduced a high-resolution equirectangular panorama (360-degree, virtual\nreality) dataset for object detection and propose a multi-projection variant of\nYOLO detector. The main challenge with equirectangular panorama image are i)\nthe lack of annotated training data, ii) high-resolution imagery and iii)\nsevere geometric distortions of objects near the panorama projection poles. In\nthis work, we solve the challenges by i) using training examples available in\nthe \"conventional datasets\" (ImageNet and COCO), ii) employing only\nlow-resolution images that require only moderate GPU computing power and\nmemory, and iii) our multi-projection YOLO handles projection distortions by\nmaking multiple stereographic sub-projections. In our experiments, YOLO\noutperforms the other state-of-art detector, Faster RCNN and our\nmulti-projection YOLO achieves the best accuracy with low-resolution input.","url_abs":"http://arxiv.org/abs/1805.08009v1","url_pdf":"http://arxiv.org/pdf/1805.08009v1.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":"object-detection-in-equirectangular-panorama","repo_url":"https://github.com/keevin60907/mp-YOLO","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"object-detection-in-equirectangular-panorama","repo_url":"https://github.com/ktlhtn/AutoSTAnnot","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":null,"task_name":"GPU"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1805.08009","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}