{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,14]],"date-time":"2026-01-14T00:45:29Z","timestamp":1768351529821,"version":"3.49.0"},"reference-count":54,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42101344"],"award-info":[{"award-number":["42101344"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Compared with general object detection with horizontal bounding boxes in natural images, oriented object detection in remote sensing images is an active and challenging research topic as objects are usually displayed in arbitrary orientations. To model the variant orientations of oriented objects, general CNN-based methods usually adopt more parameters or well-designed modules, which are often complex and inefficient. To address this issue, the detector requires two key components to deal with: (i) generating oriented proposals in a light-weight network to achieve effective representation of arbitrarily oriented objects; (ii) extracting the rotation-invariant feature map in both spatial and orientation dimensions. In this paper, we propose a novel, lightweight rotated region proposal network to produce arbitrary-oriented proposals by sliding two vertexes only on adjacent sides and adopt a simple yet effective representation to describe oriented objects. This may decrease the complexity of modeling orientation information. Meanwhile, we adopt the rotation-equivariant backbone to generate the feature map with explicit orientation channel information and utilize the spatial and orientation modules to obtain completely rotation-invariant features in both dimensions. Without tricks, extensive experiments performed on three challenging datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016 demonstrate that our proposed method can reach state-of-the-art accuracy while reducing the model size by 40% in comparison with the previous best method.<\/jats:p>","DOI":"10.3390\/rs15030594","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T05:06:14Z","timestamp":1674104774000},"page":"594","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["RiDOP: A Rotation-Invariant Detector with Simple Oriented Proposals in Remote Sensing Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Chongyang","family":"Wei","sequence":"first","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiping","family":"Ni","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Qin","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junzheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"},{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Han","family":"Zhang","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"},{"name":"College of Electronic Science, National University of Defense Technology, Changsha 410073, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Liu","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"},{"name":"School of Software Engineering, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kenan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Bian","sequence":"additional","affiliation":[{"name":"Northwest Institute of Nuclear Technology, Xi\u2019an 710024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Xia, G., Bai, X., Ding, J., Zhu, Z., Serge, B., Luo, J., Mihai, D., Marcello, P., and Zhang, L. (2018, January 18\u201321). DOTA: A large-scale dataset for object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.jvcir.2015.11.002","article-title":"Vehicle detection in aerial imagery: A small target detection benchmark","volume":"34","author":"Sebastien","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_3","unstructured":"Ding, J., Xue, N., Xia, G., Bai, X., Yang, W., Micheal, Y., Serge, B., Luo, J., Mihai, D., and Marcello, P. (2021). Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges. arXiv."},{"key":"ref_4","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (November, January 27). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G., and Lu, Q. (2019, January 15\u201321). Learning roi transformer for oriented object detection in aerial images. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Liao, W., Yang, X., Tang, J., and He, T. (2022). SCRDet++: Detecting small, cluttered and rotated objects via instance-level feature denoising and rotation loss smoothing. arXiv.","DOI":"10.1109\/TPAMI.2022.3166956"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Liu, Z., Hu, J., Weng, L., and Yang, Y. (2017, January 17\u201320). Rotated region based CNN for ship detection. Proceedings of the IEEE International Conference on Image Processing, Beijing, China.","DOI":"10.1109\/ICIP.2017.8296411"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1745","DOI":"10.1109\/LGRS.2018.2856921","article-title":"Toward Arbitrary-Oriented Ship Detection With Rotated Region Proposal and Discrimination Networks","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3111","DOI":"10.1109\/TMM.2018.2818020","article-title":"Arbitrary-Oriented Scene Text Detection via Rotation Proposals","volume":"20","author":"Ma","year":"2018","journal-title":"IEEE Trans. Multimed."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1452","DOI":"10.1109\/TPAMI.2020.2974745","article-title":"Gliding vertex on the horizontal bounding box for multi-oriented object detection","volume":"43","author":"Xu","year":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Li, J., and Xia, G. (2021). Align Deep Features for Oriented Object Detection. arXiv.","DOI":"10.1109\/TGRS.2021.3062048"},{"key":"ref_12","unstructured":"Yang, X., Liu, Q., Yan, J., Li, A., Zhang, Z., and Yu, G. (2019). R3det: Refined single-stage detector with feature refinement for rotating object. arXiv."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201318). Oriented R-CNN for Object Detection. Proceedings of the IEEE International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_14","unstructured":"Maurice, W., and Gabriele, C. (2019, January 8\u201314). General e(2)-equivariant steerable cnns. Proceedings of the Conference and Workshop on Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G. (2021, January 19\u201325). ReDet: A Rotation-equivariant Detector for Aerial Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Online.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2889","DOI":"10.1109\/TNNLS.2019.2933665","article-title":"SRGC-Nets: Sparse Repeated Group Convolutional Neural Networks","volume":"31","author":"Lu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Yuan, L., Weng, L., and Yang, Y. (2017, January 24\u201326). A High Resolution Optical Satellite Image Dataset for Ship Recognition and Some New Baselinesy. Proceedings of the International conference on pattern recognition applications and methods, Porto, Portugal.","DOI":"10.5220\/0006120603240331"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Lin, Y., Goyal, P., Ross, G., He, K., and Piotr, D. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"He, K., Georgia, G., and Ross, G. (2017, January 22\u201329). Mask r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_20","first-page":"261","article-title":"Deep learning for generic object detection: A survey","volume":"128","author":"Liu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_21","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). FCOS: Fully convolutional one-stage object detection. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_22","unstructured":"Yang, Z., Liu, S., Hu, H., Wang, L., and Stephen, L. (November, January 27). Reppoints: Point set representation for object detection. Proceedings of the IEEE International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 21\u201326). You only look once: Unified, real-time object detection. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C., and Alexander, C. (2016, January 11\u201314). SSD: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amesterdom, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1016\/j.isprsjprs.2019.11.023","article-title":"Object detection in optical remote sensing images: A survey and a new benchmark","volume":"159","author":"Li","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards real-time object detection with region proposal networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, G., Lu, S., and Zhang, W. (2019). Cad-net: A context-aware detection network for objects in remote sensing imagery. arXiv.","DOI":"10.1109\/TGRS.2019.2930982"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020, January 14\u201319). Dynamic refinement network for oriented and densely packed object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, X., and Yan, J. (2020, January 23\u201328). Arbitrary-oriented object detection with circular smooth label. Proceedings of the European Conference on Computer Vision, Online.","DOI":"10.1007\/978-3-030-58598-3_40"},{"key":"ref_31","unstructured":"Yang, X., Yang, X., Yang, J., Ming, Q., Wang, W., Tian, Q., and Yan, J. (2022, January 19\u201324). Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, K., Lin, W., See, J., Yu, H., Ke, Y., and Yang, C. (2020, January 23\u201328). PIoU Loss: Towards accurate oriented object detection in complex environments. Proceedings of the European Conference on Computer Vision, Online.","DOI":"10.1007\/978-3-030-58558-7_12"},{"key":"ref_33","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2020, January 7\u201312). Dynamic anchor learning for arbitrary oriented object detection. Proceedings of the AAAI Conference on Artificia lIntelligence, New York, NY, USA."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Yang, X., Hou, L., Yang, Z., Wang, W., and Yan, J. (2021, January 19\u201325). Dense Label Encoding for Boundary Discontinuity Free Rotation Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01556"},{"key":"ref_35","unstructured":"Taco S, C., and Welling, M. (2016, January 19\u201324). Group Equivariant Convolutional Networks. Proceedings of the International Conference on Machine Learning, New York, NY, USA."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Ye, Q., Qiu, Q., and Jiao, J. (2017, January 21\u201326). Oriented Response Networks. Proceedings of the International Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.527"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1109\/TGRS.2016.2601622","article-title":"Learning Rotation-Invariant Convolutional Neural Networks for Object Detection in VHR Optical Remote Sensing Images","volume":"54","author":"Cheng","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ren, Y., Zhu, C., and Xiao, S. (2018). Deformable Faster R-CNN with Aggregating Multi-Layer Features for Partially Occluded Object Detection in Optical Remote Sensing Images. Remote Sens., 10.","DOI":"10.3390\/rs10091470"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 21\u201326). Deep Residual Learning for Image Recognition. Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Piotr, D., and Ross, G. (2017, January 21\u201326). Feature Pyramid Networks for Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"He, X., Ma, S., He, L., Ru, L., and Wang, C. (2021). Learning Rotated Inscribed Ellipse for Oriented Object Detection in Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13183622"},{"key":"ref_42","unstructured":"Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., and Sun, Y. (2019). MMDetection: Open MMLab Detection Toolbox and Benchmark. arXiv."},{"key":"ref_43","unstructured":"Yang, J., Liu, Q., and Zhang, K. (2020, January 14\u201319). Stacked hourglass network for robust facial landmark localisation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA."},{"key":"ref_44","unstructured":"Qian, W., Yang, X., Peng, S., Guo, Y., and Y, J. (2019, January 15\u201321). Learning Modulated Loss for Rotated Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"4307","DOI":"10.1109\/TGRS.2020.3010051","article-title":"Learning center probability map for detecting objects in aerial images","volume":"59","author":"Wang","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., and Li, W. (2018, January 20\u201324). R2CNN: Rotational Region CNN for Arbitrarily-Oriented Scene Text Detection. Proceedings of the International Conference on Pattern Recognition, Beijing, China.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Wu, F., H, J., Zhou, J., Li, H., Liu, Y., and Sui, X. (2021). Improved Oriented Object Detection in Remote Sensing Images Based on a Three-Point Regression Method. Remote Sens., 13.","DOI":"10.3390\/rs13224517"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1016\/j.isprsjprs.2020.09.022","article-title":"Oriented objects as pairs of middle lines","volume":"169","author":"Wei","year":"2020","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8898","DOI":"10.1109\/JSTARS.2021.3107549","article-title":"A Refined Single-Stage Detector With Feature Enhancement and Alignment for Oriented Objects","volume":"14","author":"Chen","year":"2021","journal-title":"IEEE J. Sel. Top. Earth Obs. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Li, Y., Huang, Q., Pei, X., Jiao, L., and Shang, R. (2020). RADet: Refine Feature Pyramid Network and Multi-Layer Attention Network for Arbitrary-Oriented Object Detection of Remote Sensing Images. Remote Sens., 12.","DOI":"10.3390\/rs12030389"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, L., and Li, F. (2021). Predicting Arbitrary-Oriented Objects as Points in Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13183731"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021, January 5\u20139). Oriented Object Detection in Aerial Images with Box Boundary-Aware Vectors. Proceedings of the IEEE Winter Conference on Applications of Computer Vision, Online.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Chen, K., Pang, J., Wang, J., Xiong, Y., Li, X., Sun, S., Feng, W., Liu, Z., Shi, J., and Ouyang, W. (2019, January 15\u201321). Hybrid task cascade for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, C., Xu, C., Cui, Z., Wang, D., Jie, Z., Zhang, T., and Yang, J. (2019, January 15\u201321). Learning object-wise semantic representation for detection in remote sensing imagery. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/ICIP.2019.8803521"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/594\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:10:02Z","timestamp":1760119802000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/15\/3\/594"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,19]]},"references-count":54,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["rs15030594"],"URL":"https:\/\/doi.org\/10.3390\/rs15030594","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,19]]}}}