{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:20:50Z","timestamp":1750220450837,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2020,12,9]],"date-time":"2020-12-09T00:00:00Z","timestamp":1607472000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Research and Application of Intelligent Video Analysis in Urban Street Occupation Management, China","award":["20180411"],"award-info":[{"award-number":["20180411"]}]},{"name":"the Municipal Science and Technology Project of CQMMC, China","award":["2017030502"],"award-info":[{"award-number":["2017030502"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2020,12,9]]},"DOI":"10.1145\/3448823.3448824","type":"proceedings-article","created":{"date-parts":[[2021,3,4]],"date-time":"2021-03-04T11:33:39Z","timestamp":1614857619000},"page":"1-9","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Region-Attentioned Network with Location Scoring Dynamic-Threshold NMS for Object Detection in Remote Sensing Images"],"prefix":"10.1145","author":[{"given":"Wei","family":"Guo","sequence":"first","affiliation":[{"name":"Key Lab of Optoelectronic Technology &amp; Systems of Education Ministry Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weihong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Lab of Optoelectronic Technology &amp; Systems of Education Ministry Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiguo","family":"Gong","sequence":"additional","affiliation":[{"name":"Key Lab of Optoelectronic Technology &amp; Systems of Education Ministry Chongqing University, Chongqing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaoyue","family":"Chen","sequence":"additional","affiliation":[{"name":"HUAWEI TECHNOLOGIES CO., LTD Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,3,4]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2015.2437384"},{"key":"e_1_3_2_1_2_1","unstructured":"S Ren K He R Girshick etal 2015. Faster R-CNN: towards real-time object detection with region proposal networks[C]. neural information processing systems 91--99.  S Ren K He R Girshick et al. 2015. Faster R-CNN: towards real-time object detection with region proposal networks[C]. neural information processing systems 91--99."},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"crossref","unstructured":"G Song Y Liu X Wang. 2020. Revisiting the Sibling Head in Object Detector[J].  G Song Y Liu X Wang. 2020. Revisiting the Sibling Head in Object Detector[J].","DOI":"10.1109\/CVPR42600.2020.01158"},{"key":"e_1_3_2_1_4_1","unstructured":"A Krizhevsky I Sutskever G.E Hinton etal 2012. ImageNet Classification with Deep Convolutional Neural Networks[C]. neural information processing systems 1097--1105.  A Krizhevsky I Sutskever G.E Hinton et al. 2012. ImageNet Classification with Deep Convolutional Neural Networks[C]. neural information processing systems 1097--1105."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"J Redmon A Farhadi. 2017. YOLO9000: Better Faster Stronger[C]. computer vision and pattern recognition 6517--6525.  J Redmon A Farhadi. 2017. YOLO9000: Better Faster Stronger[C]. computer vision and pattern recognition 6517--6525.","DOI":"10.1109\/CVPR.2017.690"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TGRS.2020.2980023"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"crossref","unstructured":"R Girshick. 2015. Fast R-CNN[C]. international conference on computer vision 1440--1448.  R Girshick. 2015. Fast R-CNN[C]. international conference on computer vision 1440--1448.","DOI":"10.1109\/ICCV.2015.169"},{"key":"e_1_3_2_1_8_1","unstructured":"J Dai Y Li K He etal 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks[J]. arXiv: Computer Vision and Pattern Recognition.  J Dai Y Li K He et al. 2016. R-FCN: Object Detection via Region-based Fully Convolutional Networks[J]. arXiv: Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"crossref","unstructured":"Z Cai Q Fan R.S Feris etal 2016. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[C]. european conference on computer vision 354--370.  Z Cai Q Fan R.S Feris et al. 2016. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection[C]. european conference on computer vision 354--370.","DOI":"10.1007\/978-3-319-46493-0_22"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2018.2844175"},{"volume-title":"R Girshick, et al.","year":"2017","author":"Lin T","key":"e_1_3_2_1_11_1"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"crossref","unstructured":"W Guo W.H Li W.G Gong etal 2020. Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images[J]. Remote Sensing 12(5).  W Guo W.H Li W.G Gong et al. 2020. Extended Feature Pyramid Network with Adaptive Scale Training Strategy and Anchors for Object Detection in Aerial Images[J]. Remote Sensing 12(5).","DOI":"10.3390\/rs12050784"},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"crossref","unstructured":"B Jiang R Luo J Mao etal 2018. Acquisition of Localization Confidence for Accurate Object Detection[C]. european conference on computer vision 816--832.  B Jiang R Luo J Mao et al. 2018. Acquisition of Localization Confidence for Accurate Object Detection[C]. european conference on computer vision 816--832.","DOI":"10.1007\/978-3-030-01264-9_48"},{"volume-title":"DOTA: A Large-Scale Dataset for Object Detection in Aerial Images[C]. computer vision and pattern recognition, 3974--3983.","year":"2018","author":"Xia G","key":"e_1_3_2_1_14_1"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"crossref","unstructured":"H Tayara K.T Chong. 2018. Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network[J]. Sensors 18(10).  H Tayara K.T Chong. 2018. Object Detection in Very High-Resolution Aerial Images Using One-Stage Densely Connected Feature Pyramid Network[J]. Sensors 18(10).","DOI":"10.3390\/s18103341"},{"key":"e_1_3_2_1_16_1","unstructured":"X Yang J Yang J Yan etal 2018. R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy[J].  X Yang J Yang J Yan et al. 2018. R2CNN++: Multi-Dimensional Attention Based Rotation Invariant Detector with Robust Anchor Strategy[J]."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"G Huang Z Liu L.V Der Maaten etal 2017. Densely Connected Convolutional Networks[C]. computer vision and pattern recognition 2261--2269.  G Huang Z Liu L.V Der Maaten et al. 2017. Densely Connected Convolutional Networks[C]. computer vision and pattern recognition 2261--2269.","DOI":"10.1109\/CVPR.2017.243"},{"key":"e_1_3_2_1_18_1","unstructured":"A Van Etten. 2018. You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery[J]. arXiv: Computer Vision and Pattern Recognition.  A Van Etten. 2018. You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery[J]. arXiv: Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"crossref","unstructured":"Y Ren C Zhu S Xiao etal 2018. Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images[J]. Remote Sensing 10(9).  Y Ren C Zhu S Xiao et al. 2018. Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images[J]. Remote Sensing 10(9).","DOI":"10.3390\/rs10091470"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"J Dai H Qi Y Xiong etal 2017. Deformable Convolutional Networks[C]. international conference on computer vision 764--773.  J Dai H Qi Y Xiong et al. 2017. Deformable Convolutional Networks[C]. international conference on computer vision 764--773.","DOI":"10.1109\/ICCV.2017.89"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSTARS.2017.2694890"},{"key":"#cr-split#-e_1_3_2_1_22_1.1","doi-asserted-by":"crossref","unstructured":"Y Ren C Zhu S Xiao etal 2018. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN[J]. Applied Sciences 8","DOI":"10.3390\/app8050813"},{"key":"#cr-split#-e_1_3_2_1_22_1.2","doi-asserted-by":"crossref","unstructured":"(5) Y Ren C Zhu S Xiao et al. 2018. Small Object Detection in Optical Remote Sensing Images via Modified Faster R-CNN[J]. Applied Sciences 8(5)","DOI":"10.3390\/app8050813"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Y Jiang X Zhu X Wang etal 2017. R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.[J]. arXiv: Computer Vision and Pattern Recognition.  Y Jiang X Zhu X Wang et al. 2017. R2CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.[J]. arXiv: Computer Vision and Pattern Recognition.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"X Yang J Yang J Yan etal 2018. SCRDet: Towards More Robust Detection for Small Cluttered and Rotated Objects[J]. arXiv: Computer Vision and Pattern Recognition.  X Yang J Yang J Yan et al. 2018. SCRDet: Towards More Robust Detection for Small Cluttered and Rotated Objects[J]. arXiv: Computer Vision and Pattern Recognition.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"S.M Azimi E Vig R Bahmanyar etal 2018. Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery[J]. arXiv: Computer Vision and Pattern Recognition.  S.M Azimi E Vig R Bahmanyar et al. 2018. Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery[J]. arXiv: Computer Vision and Pattern Recognition.","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"N Bodla B Singh R Chellappa etal 2017. Soft-NMS --- Improving Object Detection with One Line of Code[C]. international conference on computer vision 5562--5570.  N Bodla B Singh R Chellappa et al. 2017. Soft-NMS --- Improving Object Detection with One Line of Code[C]. international conference on computer vision 5562--5570.","DOI":"10.1109\/ICCV.2017.593"},{"volume-title":"Softernms: Rethinking bounding box regression for accurate object detection. arXiv preprint arXiv","year":"2018","author":"He Y.H","key":"e_1_3_2_1_27_1"},{"key":"e_1_3_2_1_28_1","unstructured":"L Ma X Kan Q Xiao etal 2017.Yes-Net: An effective Detector Based on Global Information.[J]. arXiv: Computer Vision and Pattern Recognition.  L Ma X Kan Q Xiao et al. 2017.Yes-Net: An effective Detector Based on Global Information.[J]. arXiv: Computer Vision and Pattern Recognition."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"H Hu J Gu Z Zhang etal 2018. Relation Networks for Object Detection[C]. computer vision and pattern recognition 3588--3597.  H Hu J Gu Z Zhang et al. 2018. Relation Networks for Object Detection[C]. computer vision and pattern recognition 3588--3597.","DOI":"10.1109\/CVPR.2018.00378"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"X Zhang K Zhu G Chen etal 2019. Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network[J]. Remote Sensing 11(7).  X Zhang K Zhu G Chen et al. 2019. Geospatial Object Detection on High Resolution Remote Sensing Imagery Based on Double Multi-Scale Feature Pyramid Network[J]. Remote Sensing 11(7).","DOI":"10.3390\/rs11070755"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"crossref","unstructured":"Z Xu X Xu L Wang etal 2017. Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery[J]. Remote Sensing 9(12).  Z Xu X Xu L Wang et al. 2017. Deformable ConvNet with Aspect Ratio Constrained NMS for Object Detection in Remote Sensing Imagery[J]. Remote Sensing 9(12).","DOI":"10.3390\/rs9121312"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"crossref","unstructured":"J Yan H Wang M Yan etal 2019. IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery[J]. Remote Sensing 11(3).  J Yan H Wang M Yan et al. 2019. IoU-Adaptive Deformable R-CNN: Make Full Use of IoU for Multi-Class Object Detection in Remote Sensing Imagery[J]. Remote Sensing 11(3).","DOI":"10.3390\/rs11030286"},{"volume-title":"P Dollar, et al","year":"2017","author":"Xie S","key":"e_1_3_2_1_33_1"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"T Lin M Maire S Belongie etal 2014. Microsoft COCO: Common Objects in Context[C]. european conference on computer vision 740--755.  T Lin M Maire S Belongie et al. 2014. Microsoft COCO: Common Objects in Context[C]. european conference on computer vision 740--755.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"crossref","unstructured":"Y Wu K He. 2018. Group Normalization.[J]. arXiv: Computer Vision and Pattern Recognition.  Y Wu K He. 2018. Group Normalization.[J]. arXiv: Computer Vision and Pattern Recognition.","DOI":"10.1007\/978-3-030-01261-8_1"},{"volume-title":"SSD: Single Shot MultiBox Detector[C]. european conference on computer vision, 21--37.","year":"2016","author":"Liu W","key":"e_1_3_2_1_36_1"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2020.2982658"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"crossref","unstructured":"J Ding N Xue Y Long etal 2018. Learning RoI Transformer for Detecting Oriented Objects in Aerial Images.[J]. arXiv: Computer Vision and Pattern Recognition.  J Ding N Xue Y Long et al. 2018. Learning RoI Transformer for Detecting Oriented Objects in Aerial Images.[J]. arXiv: Computer Vision and Pattern Recognition.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2018.2818020"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2018.2869884"},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"X Yang H Sun K Fu etal 2018. Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks[J]. Remote Sensing 10(1).  X Yang H Sun K Fu et al. 2018. Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks[J]. Remote Sensing 10(1).","DOI":"10.3390\/rs10010132"},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"crossref","unstructured":"L Hou K Lu J Xue etal 2020. Cascade Detector With Feature Fusion For Arbitrary-Oriented Objects In Remote Sensing Images[C]\/\/ 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE.  L Hou K Lu J Xue et al. 2020. Cascade Detector With Feature Fusion For Arbitrary-Oriented Objects In Remote Sensing Images[C]\/\/ 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE.","DOI":"10.1109\/ICME46284.2020.9102807"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"C Li B Luo H Hong etal 2020. Object Detection Based on Global-Local Saliency Constraint in Aerial Images[J]. Remote Sensing 12(1435).  C Li B Luo H Hong et al. 2020. Object Detection Based on Global-Local Saliency Constraint in Aerial Images[J]. Remote Sensing 12(1435).","DOI":"10.3390\/rs12091435"}],"event":{"name":"ICVISP 2020: 2020 4th International Conference on Vision, Image and Signal Processing","acronym":"ICVISP 2020","location":"Bangkok Thailand"},"container-title":["Proceedings of the 2020 4th International Conference on Vision, Image and Signal Processing"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448823.3448824","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3448823.3448824","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:47:53Z","timestamp":1750193273000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3448823.3448824"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,9]]},"references-count":44,"alternative-id":["10.1145\/3448823.3448824","10.1145\/3448823"],"URL":"https:\/\/doi.org\/10.1145\/3448823.3448824","relation":{},"subject":[],"published":{"date-parts":[[2020,12,9]]},"assertion":[{"value":"2021-03-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}