{"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/large-selective-kernel-network-for-remote","title":"Large Selective Kernel Network for Remote Sensing Object Detection","arxiv_id":"2303.09030","date":"2023-03-16","proceeding":"ICCV 2023 1","authors":["YuXuan Li","Qibin Hou","Zhaohui Zheng","Ming-Ming Cheng","Jian Yang","Xiang Li"],"abstract":"Recent research on remote sensing object detection has largely focused on improving the representation of oriented bounding boxes but has overlooked the unique prior knowledge presented in remote sensing scenarios. Such prior knowledge can be useful because tiny remote sensing objects may be mistakenly detected without referencing a sufficiently long-range context, and the long-range context required by different types of objects can vary. In this paper, we take these priors into account and propose the Large Selective Kernel Network (LSKNet). LSKNet can dynamically adjust its large spatial receptive field to better model the ranging context of various objects in remote sensing scenarios. To the best of our knowledge, this is the first time that large and selective kernel mechanisms have been explored in the field of remote sensing object detection. Without bells and whistles, LSKNet sets new state-of-the-art scores on standard benchmarks, i.e., HRSC2016 (98.46\\% mAP), DOTA-v1.0 (81.85\\% mAP) and FAIR1M-v1.0 (47.87\\% mAP). Based on a similar technique, we rank 2nd place in 2022 the Greater Bay Area International Algorithm Competition. Code is available at https://github.com/zcablii/Large-Selective-Kernel-Network.","url_abs":"https://arxiv.org/abs/2303.09030v2","url_pdf":"https://arxiv.org/pdf/2303.09030v2.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":"large-selective-kernel-network-for-remote","repo_url":"https://github.com/zcablii/Large-Selective-Kernel-Network","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}}],"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":"oriented-object-detection","task_name":"Oriented Object Detection"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"selective-kernel","method_name":"Selective Kernel"},{"method_slug":"selective-kernel-convolution","method_name":"Selective Kernel Convolution"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/object-detection-in-aerial-images-on-hrsc2016","task":"Object Detection In Aerial Images","dataset":"HRSC2016","model":"LSKNet-S","rank_in_archive_order":3,"of":9,"metrics":{"mAP-07":"90.65","mAP-12":"98.46"},"uses_additional_data":true},{"leaderboard":"/sota/semantic-segmentation-on-loveda","task":"Semantic Segmentation","dataset":"LoveDA","model":"LSKNet-S","rank_in_archive_order":9,"of":19,"metrics":{"Category mIoU":"54.0"},"uses_additional_data":false},{"leaderboard":"/sota/semantic-segmentation-on-loveda","task":"Semantic Segmentation","dataset":"LoveDA","model":"LSKNet-T","rank_in_archive_order":12,"of":19,"metrics":{"Category mIoU":"53.2"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2303.09030","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}