{"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/tiny-object-detection-in-aerial-images","title":"Tiny Object Detection in Aerial Images","arxiv_id":null,"date":"2021-01-10","proceeding":"International Conference on Pattern Recognition (ICPR) 2021 1","authors":["Jinwang Wang","Wen Yang","Haowen Guo","Ruixiang Zhang","Gui-Song Xia"],"abstract":"Object detection in Earth Vision has achieved great progress in recent years. However, tiny object detection in aerial images remains a very challenging problem since the tiny objects contain a small number of pixels and are easily confused with the background. To advance tiny object detection research in aerial images, we present a new dataset for Tiny Object Detection in Aerial Images (AI-TOD). Specifically, AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others. To build a benchmark for tiny object detection in aerial images, we evaluate the state-of-the-art object detectors on our AI-TOD dataset. Experimental results show that direct application of these approaches on AI-TOD produces suboptimal object detection results, thus new specialized detectors for tiny object detection need to be designed. Therefore, we propose a multiple center points based learning network (M-CenterNet) to improve the localization performance of tiny object detection, and experimental results show the significant performance gain over the competitors.","url_abs":"https://github.com/jwwangchn/AI-TOD","url_pdf":"https://drive.google.com/file/d/1IiTp7gilwDCGr8QR_H9Covz8aVK7LXiI/view","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":"tiny-object-detection-in-aerial-images","repo_url":"https://github.com/jwwangchn/AI-TOD","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-in-aerial-images","task_name":"Object Detection In Aerial Images"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[{"slug":"ai-tod","name":"AI-TOD","full_name":"Tiny Object Detection in Aerial Images"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-on-ai-tod","task":"Object Detection","dataset":"AI-TOD","model":"M-CenterNet (DLA-34)","rank_in_archive_order":5,"of":7,"metrics":{"AP":"14.5","AP50":"40.7","AP75":"6.4","APm":"20.4","APs":"19.4","APt":"15.0","APvt":"6.1"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}