{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:58:33Z","timestamp":1790866713821,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T00:00:00Z","timestamp":1790035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010211","name":"Jilin Provincial Department of Education, China","doi-asserted-by":"publisher","award":["JJKH20250516BS"],"award-info":[{"award-number":["JJKH20250516BS"]}],"id":[{"id":"10.13039\/501100010211","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Rotated object detection in remote sensing imagery requires both semantic understanding and accurate geometric reasoning. Existing geometry-aware detectors commonly introduce geometric information through oriented proposals or decoder queries, making their designs dependent on specific detection architectures, while geometric relationships among feature tokens remain insufficiently explored. To address this limitation, we propose GCGFormer, a geometry-compatibility-guided attention module for rotated object detection. Specifically, GCGFormer constructs a dual-token representation and a geometry compatibility matrix to model pairwise spatial relationships, including positional proximity, aspect ratio consistency, and orientation alignment. The matrix is integrated into self-attention to jointly optimize semantic similarity and geometric coherence. The proposed module can be readily integrated into existing detectors without extensive modifications. As an architecture-independent module placed before the detection head, GCGFormer can be integrated into different rotated object detectors. Experiments on DOTA-v1.0 show that GCGFormer improves the mAP of Oriented R-CNN from 65.1% to 65.6%, Rotated Faster R-CNN from 63.3% to 63.8%, Rotated ATSS from 61.7% to 62.1%, S2ANet from 62.7% to 63.1%, and YOLOv8-OBB from 72.7% to 72.9%. These consistent, although modest, improvements demonstrate that explicit feature-token geometric compatibility provides a generally applicable complement to semantic attention for rotated object detection.<\/jats:p>","DOI":"10.3390\/s26195995","type":"journal-article","created":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:23:56Z","timestamp":1790864636000},"page":"5995","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GCGFormer: Geometry-Compatibility-Guided Attention for Rotated Object Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-2451-5695","authenticated-orcid":false,"given":"Huiji","family":"Wang","sequence":"first","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"methodology","vocabulary":"credit"},{"role":"conceptualization","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-4225-0239","authenticated-orcid":false,"given":"Dandan","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-7084-6650","authenticated-orcid":false,"given":"Zhi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"Institute of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"supervision","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"}]},{"given":"Zhichao","family":"Han","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"},{"name":"Jilin Provincial Urban Water Supply Co., Ltd., Changchun 130000, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Yunhe","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"investigation","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"}]},{"given":"Chunpeng","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Electronic Information Engineering, Changchun University of Science and Technology, Changchun 130022, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"data-curation","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gui, S., Song, S., Qin, R., and Tang, Y. (2024). Remote sensing object detection in the deep learning era\u2014A review. Remote Sens., 16.","DOI":"10.3390\/rs16020327"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bai, C., Bai, X., and Wu, K. (2023). A review: Remote sensing image object detection algorithm based on deep learning. Electronics, 12.","DOI":"10.3390\/electronics12244902"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"113416","DOI":"10.1016\/j.engappai.2025.113416","article-title":"Enhancing remote sensing object detection via selective-perspective-class integration","volume":"165","author":"Wang","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2023.3312347","article-title":"Remote sensing object detection meets deep learning: A metareview of challenges and advances","volume":"11","author":"Zhang","year":"2023","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_5","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":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Yang, X., Yan, J., Feng, Z., and He, T. (2021, January 2\u20139). R3det: Refined single-stage detector with feature refinement for rotating object. Proceedings of the AAAI Conference on Artificial Intelligence, Virtual.","DOI":"10.1609\/aaai.v35i4.16426"},{"key":"ref_7","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_8","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wang, H., Wang, L., Pan, C., Liu, Q., and Wang, X. (2021). Constraint loss for rotated object detection in remote sensing images. Remote Sens., 13.","DOI":"10.3390\/rs13214291"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, Q., and Yu, C. (2022). Point RCNN: An angle-free framework for rotated object detection. Remote Sens., 14.","DOI":"10.3390\/rs14112605"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_11","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., and Gelly, S. (2021, January 3\u20137). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. Proceedings of the International Conference on Learning Representations, Virtual."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1109\/TCSVT.2022.3222906","article-title":"AO2-DETR: Arbitrary-oriented object detection transformer","volume":"33","author":"Dai","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Zhao, J., Ding, Z., Zhou, Y., Zhu, H., Du, W.L., Yao, R., and El Saddik, A. (2024). OrientedFormer: An end-to-end transformer-based oriented object detector in remote sensing images. IEEE Trans. Geosci. Remote Sens., 62.","DOI":"10.1109\/TGRS.2024.3456240"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1007\/s11227-025-07247-1","article-title":"GA-DETR: A lightweight and efficient infrared target detection method for UAVs","volume":"81","author":"Wu","year":"2025","journal-title":"J. Supercomput."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, H., Ma, Z., and Li, X. (2024). Rs-detr: An improved remote sensing object detection model based on rt-detr. Appl. Sci., 14.","DOI":"10.3390\/app142210331"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"133335","DOI":"10.1109\/ACCESS.2021.3110888","article-title":"Feature learning improved by location guidance and supervision for object detection","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_17","first-page":"1","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., and Guo, B. (2021, January 11\u201317). Swin transformer: Hierarchical vision transformer using shifted windows. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"ref_19","unstructured":"Katharopoulos, A., Vyas, A., Pappas, N., and Fleuret, F. (2020, January 13\u201318). Transformers are rnns: Fast autoregressive transformers with linear attention. Proceedings of the International Conference on Machine Learning. PMLR, Virtual."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"16344","DOI":"10.52202\/068431-1189","article-title":"Flashattention: Fast and memory-efficient exact attention with io-awareness","volume":"35","author":"Dao","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). Cbam: Convolutional block attention module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 19\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_23","unstructured":"Mehta, S., and Rastegari, M. (2022, January 25\u201329). MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer. Proceedings of the International Conference on Learning Representations (ICLR), Virtual."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jin, X., Su, H., Ma, C., Liu, K., Wu, W., Hui, F., and Yan, J. (2025, January 19\u201325). GeoFormer: Geometry Point Encoder for 3D Object Detection with Graph-based Transformer. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Honolulu, HI, USA.","DOI":"10.1109\/ICCV51701.2025.02495"},{"key":"ref_25","unstructured":"Tran-Dubois, L. (2025, January 19\u201323). Injecting Geometric Scene Priors into Vision Transformers for Improved 2D-3D Understanding. Proceedings of the Structural Priors for Vision Workshop at ICCV, Honolulu, HI, USA."},{"key":"ref_26","unstructured":"Atzeni, M., Sachan, M., and Loukas, A. (2023, January 23\u201329). Infusing lattice symmetry priors in attention mechanisms for sample-efficient abstract geometric reasoning. Proceedings of the International Conference on Machine Learning. PMLR, Honolulu, HI, USA."},{"key":"ref_27","unstructured":"Hu, W., Lin, J., Long, Y., Ran, Y., Jiang, L., Wang, Y., Zhu, C., Xu, R., Wang, T., and Pang, J. (2026, January 3\u20137). G2VLM: Geometry Grounded Vision Language Model with Unified 3D Reconstruction and Spatial Reasoning. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Denver, CO, USA."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Zhang, R., Chen, K., Li, L., Zhou, D., Xu, Y., Lin, Z., Xu, L., and Song, W. (2026). Band-Mixed Edge-Aware Interaction Learning for RGB-T Camouflaged Object Detection. IEEE Trans. Multimed., 1\u201312.","DOI":"10.1109\/TMM.2026.3703589"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021, January 19\u201325). Redet: A rotation-equivariant detector for aerial object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.M., Yang, J., and Li, X. (2023). Large selective kernel network for remote sensing object detection. Proceedings of the 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"ref_31","unstructured":"Wang, X., Wang, G., Dang, Q., Liu, Y., Hu, X., and Yu, D. (2022). PP-YOLOE-R: An efficient anchor-free rotated object detector. arXiv."},{"key":"ref_32","unstructured":"Ma, T., Mao, M., Zheng, H., Gao, P., Wang, X., Han, S., Ding, E., Zhang, B., and Doermann, D. (2021). Oriented object detection with transformer. arXiv."},{"key":"ref_33","first-page":"1","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","volume":"28","author":"Ren","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhou, Y., Yang, X., Zhang, G., Wang, J., Liu, Y., Hou, L., Jiang, X., Liu, X., Yan, J., and Lyu, C. (2022, January 10\u201314). Mmrotate: A rotated object detection benchmark using pytorch. Proceedings of the 30th ACM International Conference on Multimedia, Lisbon, Portugal.","DOI":"10.1145\/3503161.3548541"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 11\u201317). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2020, January 13\u201319). Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_37","unstructured":"Jocher, G., Chaurasia, A., and Qiu, J. (2026, September 14). Ultralytics YOLOv8. Available online: https:\/\/github.com\/ultralytics\/ultralytics."},{"key":"ref_38","first-page":"5602511","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/5995\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T14:33:23Z","timestamp":1790865203000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/19\/5995"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,22]]},"references-count":38,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2026,10]]}},"alternative-id":["s26195995"],"URL":"https:\/\/doi.org\/10.3390\/s26195995","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,22]]}}}