{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T03:23:16Z","timestamp":1774495396817,"version":"3.50.1"},"reference-count":59,"publisher":"Association for Computing Machinery (ACM)","issue":"10","license":[{"start":{"date-parts":[[2024,10,30]],"date-time":"2024-10-30T00:00:00Z","timestamp":1730246400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62271336 and 62211530110"],"award-info":[{"award-number":["62271336 and 62211530110"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Research Program Project of Sichuan Province","award":["2024YFHZ0289"],"award-info":[{"award-number":["2024YFHZ0289"]}]},{"name":"Opening Foundation of Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, China","award":["TJUT-CVS20220001"],"award-info":[{"award-number":["TJUT-CVS20220001"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"crossref","award":["SCU2023D062"],"award-info":[{"award-number":["SCU2023D062"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Multimedia Comput. Commun. Appl."],"published-print":{"date-parts":[[2024,10,31]]},"abstract":"<jats:p>\n            Object detection in remote sensing image (RSI) research has seen significant advancements, particularly with the advent of deep learning. However, challenges such as orientation, scale, aspect ratio variations, dense object distribution, and category imbalances remain. To address these challenges, we present DAG-YOLO, a\n            <jats:italic>one-stage context-feature adaptive weighted fusion network<\/jats:italic>\n            that incorporates through three innovative parts. First, we integrate\n            <jats:italic>1D Gaussian Angle-coding<\/jats:italic>\n            with YOLOv5 to convert the angle regression task into a classification task, establishing a more robust rotating object detection baseline, GLR-YOLO. Second, we introduce the Dual Branch Context Adaptive Modeling module, which enhances feature extraction capabilities by capturing global context information. Third, we design an adaptive detect head with the Adaptive Global Feature Aggregation and Reweighting (AGFAR) module. AGFAR addresses feature inconsistency among different output layers of the Feature Pyramid Network, retaining useful semantic information and elevating detection accuracy. Extensive experiments on public datasets DOTA-v1.0, DOTA-v1.5, and UCAS-AOD showcase mAP scores of 77.75%, 73.79%, and 90.27%, respectively. Our proposed method has the best performance among the current mainstream SOTA methods, which proves its effectiveness in RSI object detection.\n          <\/jats:p>","DOI":"10.1145\/3674978","type":"journal-article","created":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T19:53:12Z","timestamp":1719517992000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["DAG-YOLO: A Context-Feature Adaptive fusion Rotating Detection Network in Remote Sensing Images"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2469-3995","authenticated-orcid":false,"given":"Zhenjiang","family":"Guo","sequence":"first","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8399-3172","authenticated-orcid":false,"given":"Xiaohai","family":"He","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0324-6016","authenticated-orcid":false,"given":"Yu","family":"Yang","sequence":"additional","affiliation":[{"name":"Dtaic Inspection Equipment (Dazhou) Co., Ltd., Dazhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3555-0005","authenticated-orcid":false,"given":"Linbo","family":"Qing","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6200-5147","authenticated-orcid":false,"given":"Honggang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Electronics and Information Engineering, Sichuan University, Chengdu, China and Key Laboratory of Computer Vision and System, Ministry of Education, Tianjin University of Technology, Tianjin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,30]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW.2019.00246"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1145\/3597612"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2021.3105551"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPRW56347.2022.00472"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSVT.2022.3222906"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00667"},{"key":"e_1_3_1_8_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11263-014-0733-5"},{"key":"e_1_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2014.81"},{"key":"e_1_3_1_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00868"},{"key":"e_1_3_1_11_2","first-page":"1","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han Jiaming","year":"2021","unstructured":"Jiaming Han, Jian Ding, Jie Li, and Gui-Song Xia. 2021a. Align deep features for oriented object detection. IEEE Transactions on Geoscience and Remote Sensing 60 (2021), 1\u201311.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_1_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"e_1_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"e_1_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs15030757"},{"key":"e_1_3_1_15_2","doi-asserted-by":"publisher","DOI":"10.1145\/3418213"},{"key":"e_1_3_1_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV51458.2022.00348"},{"key":"e_1_3_1_17_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-01264-9_45"},{"key":"e_1_3_1_18_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs14051246"},{"key":"e_1_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00187"},{"key":"e_1_3_1_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"e_1_3_1_21_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.106"},{"key":"e_1_3_1_22_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_1_23_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIP.2022.3231058"},{"key":"e_1_3_1_24_2","doi-asserted-by":"publisher","DOI":"10.1155\/2022\/8515510"},{"key":"e_1_3_1_25_2","unstructured":"Songtao Liu Di Huang and Yunhong Wang. 2019. Learning spatial fusion for single-shot object detection. arXiv:1911.09516. Retrieved from https:\/\/arxiv.org\/abs\/1911.09516"},{"key":"e_1_3_1_26_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.00125"},{"key":"e_1_3_1_27_2","unstructured":"Chengqi Lyu Wenwei Zhang Haian Huang Yue Zhou Yudong Wang Yanyi Liu Shilong Zhang and Kai Chen. 2022. RTMDet: An empirical study of designing real-time object detectors. arXiv:2212.07784. Retrieved from https:\/\/arxiv.org\/abs\/2212.07784"},{"key":"e_1_3_1_28_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2023.01.001"},{"key":"e_1_3_1_29_2","doi-asserted-by":"publisher","DOI":"10.3390\/rs13142664"},{"key":"e_1_3_1_30_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"e_1_3_1_31_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.91"},{"key":"e_1_3_1_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3506853"},{"key":"e_1_3_1_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2016.2577031"},{"key":"e_1_3_1_34_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2017.74"},{"key":"e_1_3_1_35_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10489-022-03393-8"},{"key":"e_1_3_1_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"e_1_3_1_37_2","unstructured":"Jinwang Wang Chang Xu Wen Yang and Lei Yu. 2021. A normalized Gaussian Wasserstein distance for tiny object detection. arXiv:2110.13389. Retrieved from https:\/\/arxiv.org\/abs\/2110.13389"},{"key":"e_1_3_1_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2018.00418"},{"key":"e_1_3_1_39_2","unstructured":"Xingxing Xie Gong Cheng Qingyang Li Shicheng Miao Ke Li and Junwei Han. 2022. Fewer is more: Efficient object detection in large aerial images. arXiv:2212.13136. Retrieved from https:\/\/arxiv.org\/abs\/2212.13136"},{"key":"e_1_3_1_40_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"e_1_3_1_41_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.2974745"},{"key":"e_1_3_1_42_2","doi-asserted-by":"publisher","DOI":"10.1145\/3472393"},{"key":"e_1_3_1_43_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"e_1_3_1_44_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"e_1_3_1_45_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"e_1_3_1_46_2","first-page":"11830","volume-title":"Proceedings of the 38th International Conference on Machine Learning","author":"Yang Xue","year":"2021","unstructured":"Xue Yang, Junchi Yan, Qi Ming, Wentao Wang, Xiaopeng Zhang, and Qi Tian. 2021c. Rethinking rotated object detection with gaussian wasserstein distance loss. In Proceedings of the 38th International Conference on Machine Learning. PMLR, 11830\u201311841."},{"key":"e_1_3_1_47_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00832"},{"key":"e_1_3_1_48_2","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang Xue","year":"2021","unstructured":"Xue Yang, Xiaojiang Yang, Jirui Yang, Qi Ming, Wentao Wang, Qi Tian, and Junchi Yan. 2021d. Learning high-precision bounding box for rotated object detection via kullback-leibler divergence. Advances in Neural Information Processing Systems 34 (2021), 18381\u201318394.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_49_2","unstructured":"Xue Yang Yue Zhou Gefan Zhang Jirui Yang Wentao Wang Junchi Yan Xiaopeng Zhang and Qi Tian. 2022b. The KFIoU loss for rotated object detection. arXiv:2201.12558. Retrieved from https:\/\/arxiv.org\/abs\/2201.12558"},{"key":"e_1_3_1_50_2","first-page":"1","article-title":"On improving bounding box representations for oriented object detection","volume":"61","author":"Yao Yanqing","year":"2022","unstructured":"Yanqing Yao, Gong Cheng, Guangxing Wang, Shengyang Li, Peicheng Zhou, Xingxing Xie, and Junwei Han. 2022. On improving bounding box representations for oriented object detection. IEEE Transactions on Geoscience and Remote Sensing 61 (2022), 1\u201311.","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"e_1_3_1_51_2","doi-asserted-by":"publisher","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"e_1_3_1_52_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.JRS.16.034510"},{"key":"e_1_3_1_53_2","unstructured":"Yi Yu Xue Yang Qingyun Li Yue Zhou Gefan Zhang Junchi Yan and Feipeng Da. 2023. H2RBox-v2: Boosting hbox-supervised oriented object detection via symmetric learning. arXiv:2304.04403. Retrieved from https:\/\/arxiv.org\/abs\/2304.04403"},{"key":"e_1_3_1_54_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109938"},{"key":"e_1_3_1_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3513133"},{"key":"e_1_3_1_56_2","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2022.3225843"},{"key":"e_1_3_1_57_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i07.6999"},{"key":"e_1_3_1_58_2","first-page":"11","article-title":"Point RCNN: An angle-free framework for rotated object detection","volume":"14","author":"Zhou Qiang","year":"2022","unstructured":"Qiang Zhou and Chaohui Yu. 2022. Point RCNN: An angle-free framework for rotated object detection. Remote Sensing 14, 11 (2022), 2605.","journal-title":"Remote Sensing"},{"key":"e_1_3_1_59_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-58545-7_6"},{"key":"e_1_3_1_60_2","doi-asserted-by":"publisher","DOI":"10.1109\/DSInS54396.2021.9670625"}],"container-title":["ACM Transactions on Multimedia Computing, Communications, and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674978","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3674978","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:05:56Z","timestamp":1750291556000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3674978"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,30]]},"references-count":59,"journal-issue":{"issue":"10","published-print":{"date-parts":[[2024,10,31]]}},"alternative-id":["10.1145\/3674978"],"URL":"https:\/\/doi.org\/10.1145\/3674978","relation":{},"ISSN":["1551-6857","1551-6865"],"issn-type":[{"value":"1551-6857","type":"print"},{"value":"1551-6865","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,10,30]]},"assertion":[{"value":"2023-07-26","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-06-20","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2024-10-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}