{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T06:03:00Z","timestamp":1771048980779,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T00:00:00Z","timestamp":1770595200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Science and Technology Project of State Grid Hebei Information and Telecommunication Branch","award":["kj2024-018"],"award-info":[{"award-number":["kj2024-018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Rotated object detection aims to achieve precise localization by strictly aligning bounding boxes with object orientations, thereby minimizing background interference. Existing methods predominantly focus on extracting intra-object features within rotated bounding boxes. However, these approaches often overlook the discriminative contextual information from the surrounding background, leading to classification ambiguity when internal features are indistinguishable. To address this limitation, we propose Background Information Fusion R-CNN (BIF-RCNN), a novel rotated object detection framework that strategically re-integrates the background context from the object\u2019s horizontal enclosing region to validate its category, turning previously discarded \u201cnoise\u201d into auxiliary discriminative cues. Specifically, we introduce a dual-level rotation-horizontal feature fusion module (DFM), which leverages horizontal bounding boxes enclosing the rotated objects to extract contextual background features. These features are then adaptively fused with the internal object features to enhance the overall representation capability of the model. In addition, we design a Prediction Difference and Entropy-Constrained Loss (PDE Loss), which guides the model to focus on hard-to-classify samples that are prone to confusion due to similar feature representations. This loss function improves the model\u2019s robustness and discriminative power. Extensive experiments conducted on the DOTA benchmark dataset demonstrate the effectiveness of the proposed method. Notably, our approach achieves up to a 4.02% AP improvement in single-category detection performance compared to a strong baseline, highlighting its superiority in rotated object detection tasks.<\/jats:p>","DOI":"10.3390\/a19020139","type":"journal-article","created":{"date-parts":[[2026,2,9]],"date-time":"2026-02-09T14:05:15Z","timestamp":1770645915000},"page":"139","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["BIF-RCNN: Fusing Background Information for Rotated Object Detection"],"prefix":"10.3390","volume":"19","author":[{"given":"Jianbin","family":"Zhao","sequence":"first","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4564-470X","authenticated-orcid":false,"given":"Xing","family":"Xu","sequence":"additional","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shaoying","family":"Wang","sequence":"additional","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Grid Hebei Information and Telecommunication Branch, Shijiazhuang 050011, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shengyi","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Zeng","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiangshuai","family":"Bu","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yiran","family":"Shen","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiwen","family":"Xue","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Zong","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0707-2176","authenticated-orcid":false,"given":"Guoxin","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6769-3177","authenticated-orcid":false,"given":"Zhonghong","family":"Ou","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6626-9932","authenticated-orcid":false,"given":"Meina","family":"Song","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7695-1633","authenticated-orcid":false,"given":"Yifan","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,9]]},"reference":[{"key":"ref_1","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":"2016","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Zhang, G., Ou, Z., Xue, K., Sun, J., Zhu, Y., Yao, S., Shen, Y., and Song, M. (2025). DGFSD: Bridging the Gap between Dense and Sparse for Fully Sparse 3D Object Detection. Proceedings of the 33rd ACM International Conference on Multimedia, Dublin, Ireland,27\u201331 October 2025, Association for Computing Machinery.","DOI":"10.1145\/3746027.3755552"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhang, G., Song, Z., Liu, L., and Ou, Z. (2025). FGU3R: Fine-Grained Fusion via Unified 3D Representation for Multimodal 3D Object Detection. Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 6\u201311 April 2025, IEEE.","DOI":"10.1109\/ICASSP49660.2025.10889148"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"111882","DOI":"10.1016\/j.patcog.2025.111882","article-title":"Domain incremental learning for object detection","volume":"170","author":"Luo","year":"2026","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"103575","DOI":"10.1016\/j.inffus.2025.103575","article-title":"Object detection with multimodal large vision-language models: An in-depth review","volume":"126","author":"Sapkota","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"103414","DOI":"10.1016\/j.inffus.2025.103414","article-title":"COMO: Cross-mamba interaction and offset-guided fusion for multimodal object detection","volume":"125","author":"Liu","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"618","DOI":"10.1016\/j.trpro.2025.03.116","article-title":"Challenges and Innovations in 3D Object Recognition: The Integration of LiDAR and Camera Sensors for Autonomous Applications","volume":"84","author":"Zahid","year":"2025","journal-title":"Transp. Res. Procedia"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021). Oriented R-CNN for object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10\u201317 October 2021, IEEE.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G.S., and Lu, Q. (2019). Learning RoI transformer for oriented object detection in aerial images. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 22\u201329 October 2017, IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_13","first-page":"5602511","article-title":"Align deep features for oriented object detection","volume":"60","author":"Han","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"5610315","DOI":"10.1109\/TGRS.2024.3364713","article-title":"ARS-DETR: Aspect ratio-sensitive detection transformer for aerial oriented object detection","volume":"62","author":"Zeng","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","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 IEEE\/CVF International Conference on Computer Vision (ICCV), Paris, France, 1\u20136 October 2023, IEEE.","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G.S. (2021). Redet: A rotation-equivariant detector for aerial object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20\u201325 June 2021, IEEE.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Su, W., and Jing, D. (2025). DDL R-CNN: Dynamic direction learning R-CNN for rotated object detection. Algorithms, 18.","DOI":"10.3390\/a18010021"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Ming, Q., Zhou, Z., Miao, L., Zhang, H., and Li, L. (2020). Dynamic anchor learning for arbitrary-oriented object detection. arXiv.","DOI":"10.1609\/aaai.v35i3.16336"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"7089","DOI":"10.1109\/TMM.2022.3217397","article-title":"Free3Net: Gliding Free, Orientation Free, and Anchor Free Network for Oriented Object Detection","volume":"25","author":"Ou","year":"2022","journal-title":"IEEE Trans. Multimed."},{"key":"ref_20","first-page":"1879","article-title":"ASL-OOD: Hierarchical Contextual Feature Fusion with Angle-Sensitive Loss for Oriented Object Detection","volume":"82","author":"Wang","year":"2025","journal-title":"Comput. Mater. Contin."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, W., Cai, Y., Luo, Z., Liu, W., Wang, T., and Li, Z. (2024). SA3Det: Detecting Rotated Objects via Pixel-Level Attention and Adaptive Labels Assignment. Remote Sens., 16.","DOI":"10.3390\/rs16132496"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.isprsjprs.2025.06.029","article-title":"RO2-DETR: Rotation-equivariant oriented object detection transformer with 1D rotated convolution kernel","volume":"228","author":"Dang","year":"2025","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, S., Jiang, H., Yang, J., Ma, X., and Chen, J. (2024). AMFEF-DETR: An End-to-End Adaptive Multi-Scale Feature Extraction and Fusion Object Detection Network Based on UAV Aerial Images. Drones, 8.","DOI":"10.3390\/drones8100523"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2510","DOI":"10.1038\/s41598-025-86601-y","article-title":"A multi-scale rotated ship targets detection network for remote sensing images in complex scenarios","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhou, W., Liu, X., Zheng, Y., Zhang, D., and Xiang, H. (2024). AFPN Based YOLOX for Rotation Object Detection in Remote Sensing Image. Proceedings of the 2024 China Automation Congress (CAC), Qingdao, China, 1\u20133 November 2024, IEEE.","DOI":"10.1109\/CAC63892.2024.10864988"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"129432","DOI":"10.1016\/j.neucom.2025.129432","article-title":"Enhancing rotated object detection via anisotropic Gaussian bounding box and Bhattacharyya distance","volume":"623","author":"Thai","year":"2025","journal-title":"Neurocomputing"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"105381","DOI":"10.1016\/j.imavis.2024.105381","article-title":"FPDIoU Loss: A loss function for efficient bounding box regression of rotated object detection","volume":"154","author":"Ma","year":"2025","journal-title":"Image Vis. Comput."},{"key":"ref_28","first-page":"6001705","article-title":"Ellipse IoU Loss: Better Learning for Rotated Bounding Box Regression","volume":"21","author":"Li","year":"2024","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Xia, G.S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo, J., Datcu, M., Pelillo, M., and Zhang, L. (2018). 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, 18\u201323 June 2018, IEEE.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The pascal visual object classes (voc) challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (2017). Feature pyramid networks for object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21\u201326 July 2017, IEEE.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yi, J., Wu, P., Liu, B., Huang, Q., Qu, H., and Metaxas, D. (2021). Oriented object detection in aerial images with box boundary-aware vectors. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Virtual, 5\u20139 January 2021, IEEE.","DOI":"10.1109\/WACV48630.2021.00220"},{"key":"ref_34","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_35","unstructured":"Lin, Y., Feng, P., Guan, J., Wang, W., and Chambers, J. (2019). IENet: Interacting embranchment one stage anchor free detector for orientation aerial object detection. arXiv."},{"key":"ref_36","unstructured":"Yang, X., Zhou, Y., Zhang, G., Yang, J., Wang, W., Yan, J., Zhang, X., and Tian, Q. (2022). The KFIoU loss for rotated object detection. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Chen, Z., Chen, K., Lin, W., See, J., Yu, H., Ke, Y., and Yang, C. (2020). Piou loss: Towards accurate oriented object detection in complex environments. Proceedings of the European Conference on Computer Vision (ECCV), Springer.","DOI":"10.1007\/978-3-030-58558-7_12"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Pan, X., Ren, Y., Sheng, K., Dong, W., Yuan, H., Guo, X., Ma, C., and Xu, C. (2020). Dynamic refinement network for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA, 13\u201319 June 2020, IEEE.","DOI":"10.1109\/CVPR42600.2020.01122"},{"key":"ref_39","first-page":"2458","article-title":"Learning modulated loss for rotated object detection","volume":"Volume 35","author":"Qian","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence, Virtual, 19\u201321 May 2021"},{"key":"ref_40","first-page":"5605814","article-title":"CFC-Net: A critical feature capturing network for arbitrary-oriented object detection in remote-sensing images","volume":"60","author":"Ming","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"7869","DOI":"10.1109\/TCSVT.2022.3186070","article-title":"RSDet++: Point-based modulated loss for more accurate rotated object detection","volume":"32","author":"Qian","year":"2022","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Guo, Z., Liu, C., Zhang, X., Jiao, J., Ji, X., and Ye, Q. (2021). Beyond bounding-box: Convex-hull feature adaptation for oriented and densely packed object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA, 20\u201325 June 2021, IEEE.","DOI":"10.1109\/CVPR46437.2021.00868"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"5618010","DOI":"10.1109\/TGRS.2024.3385985","article-title":"Oriented object detection via contextual dependence mining and penalty-incentive allocation","volume":"62","author":"Xie","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Zhu, X., Wang, X., Yang, S., Li, W., Wang, H., Fu, P., and Luo, Z. (2017). R2CNN: Rotational region CNN for orientation robust scene text detection. arXiv.","DOI":"10.1109\/ICPR.2018.8545598"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Yang, X., Yang, J., Yan, J., Zhang, Y., Zhang, T., Guo, Z., Sun, X., and Fu, K. (2019). Scrdet: Towards more robust detection for small, cluttered and rotated objects. Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea, 27 October\u20132 November 2019, IEEE.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"5625411","DOI":"10.1109\/TGRS.2022.3183022","article-title":"Anchor-free oriented proposal generator for object detection","volume":"60","author":"Cheng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/139\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T05:13:52Z","timestamp":1771046032000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/2\/139"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,9]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2026,2]]}},"alternative-id":["a19020139"],"URL":"https:\/\/doi.org\/10.3390\/a19020139","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,2,9]]}}}