{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:17:27Z","timestamp":1780082247503,"version":"3.54.0"},"reference-count":62,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T00:00:00Z","timestamp":1774310400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2024MD753953"],"award-info":[{"award-number":["2024MD753953"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005047","name":"Natural Science Foundation of Liaoning Province","doi-asserted-by":"publisher","award":["2024-BS-262"],"award-info":[{"award-number":["2024-BS-262"]}],"id":[{"id":"10.13039\/501100005047","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Liaoning Provincial Science and Technology Plan Project of China","award":["2023JH1\/10400099"],"award-info":[{"award-number":["2023JH1\/10400099"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Synthetic aperture radar (SAR) imagery plays a vital role in critical applications such as military reconnaissance and disaster monitoring. These applications require high detection accuracy. Therefore, rotated object detection has gained increasing attention. By predicting an object orientation angle, it offers advantages over horizontal bounding boxes, especially for elongated structures such as ships and bridges in SAR scenes. However, challenges such as speckle noise and complex backgrounds in SAR imagery still hinder high-precision detection. To address this, we propose WE-KAN, a novel rotated object detection framework based on wavelet features and Kolmogorov\u2013Arnold network (KAN) prediction. First, we enhance the backbone by incorporating wavelet domain features from SAR grayscale images. The extracted wavelet domain features and image features are fused by a proposed attention module. Second, considering the sensitivity to angle prediction, we design a angle predictor based on KAN. This architecture provides a powerful and dedicated solution for accurate angle regression. Finally, for precise rotated bounding box regression, we employ a joint loss function combining a rotated intersection over union (RIoU) with a Gaussian distance loss function. These designs improve the model\u2019s robustness to noise and its perception of fine object structures. When evaluated on the large-scale public RSAR dataset, our method achieves an AP50 of 70.1 and a mAP of 35.9 under the same training schedule and backbone network, significantly outperforming existing baselines. This demonstrates the effectiveness and robustness of our method for dense, small, and highly oriented objects in complex SAR scenes.<\/jats:p>","DOI":"10.3390\/s26072011","type":"journal-article","created":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T12:58:59Z","timestamp":1774357139000},"page":"2011","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["WE-KAN: SAR Image Rotated Object Detection Method Based on Wavelet Domain Feature Enhancement and KAN Prediction Head"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7780-3213","authenticated-orcid":false,"given":"Mingchun","family":"Li","sequence":"first","affiliation":[{"name":"School of Intelligent Science and Information Engineering, Shenyang University, Shenyang 110044, China"},{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1833-1708","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Information Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5911-4790","authenticated-orcid":false,"given":"Qiang","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Intelligent Science and Information Engineering, Shenyang University, Shenyang 110044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0787-825X","authenticated-orcid":false,"given":"Dali","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1109\/MGRS.2021.3097894","article-title":"Spaceborne synthetic aperture radar imaging algorithms: An overview","volume":"10","author":"Sun","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"117342","DOI":"10.1016\/j.eswa.2022.117342","article-title":"SAR data applications in earth observation: An overview","volume":"205","author":"Tsokas","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Hu, C., Li, Y., Chen, Z., Liu, F., Zhang, Q., Monti-Guarnieri, A.V., Hobbs, S., Anghel, A., and Datcu, M. (2025). Distributed Spaceborne SAR: A review of systems, applications, and the road ahead. IEEE Geosci. Remote Sens. Mag., in press.","DOI":"10.1109\/MGRS.2025.3535412"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"5236217","DOI":"10.1109\/TGRS.2022.3213305","article-title":"Scattering model guided adversarial examples for SAR target recognition: Attack and defense","volume":"60","author":"Peng","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9106","DOI":"10.1109\/JSTARS.2023.3310363","article-title":"Synthetic aperture radar (SAR) for ocean: A review","volume":"16","author":"Asiyabi","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","first-page":"103771","article-title":"YOLOv8-BYTE: Ship tracking algorithm using short-time sequence SAR images for disaster response leveraging GeoAI","volume":"128","author":"Yasir","year":"2024","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2994","DOI":"10.1038\/s41598-025-32855-5","article-title":"Patch-sampled contrastive learning for dense prediction pretraining in metallographic images","volume":"16","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6158","DOI":"10.1109\/TNNLS.2021.3133760","article-title":"Semisupervised boundary detection for aluminum grains combined with transfer learning and region growing","volume":"34","author":"Li","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_9","first-page":"5211314","article-title":"Category-oriented localization distillation for sar object detection and a unified benchmark","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zhang, T., Zhang, X., Li, J., Xu, X., Wang, B., Zhan, X., Xu, Y., Ke, X., Zeng, T., and Su, H. (2021). SAR ship detection dataset (SSDD): Official release and comprehensive data analysis. Remote Sens., 13.","DOI":"10.3390\/rs13183690"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Peter, E., Ang, L.M., Seng, K.P., and Srivastava, S. (2026). Recent Advances in Deep Learning for SAR Images: Overview of Methods, Challenges, and Future Directions. Sensors, 26.","DOI":"10.3390\/s26041143"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"110720","DOI":"10.1016\/j.dib.2024.110720","article-title":"A labelled dataset for rebar counting inspection on construction sites using unmanned aerial vehicles","volume":"55","author":"Wang","year":"2024","journal-title":"Data Brief"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"110885","DOI":"10.1016\/j.dib.2024.110885","article-title":"Building fa\u00e7ade datasets for analyzing building characteristics using deep learning","volume":"57","author":"Wang","year":"2024","journal-title":"Data Brief"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zunair, H., Khan, S., and Hamza, A.B. (2024). RSUD20K: A dataset for road scene understanding in autonomous driving. Proceedings of the IEEE International Conference on Image Processing (ICIP), Abu Dhabi, UAE, 27\u201330 October 2024, IEEE.","DOI":"10.1109\/ICIP51287.2024.10648203"},{"key":"ref_15","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_16","first-page":"581","article-title":"RSDD-SAR: Rotated ship detection dataset in SAR images","volume":"11","author":"Xu","year":"2022","journal-title":"J. Radars"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Zhang, X., Yang, X., Li, Y., Yang, J., Cheng, M.M., and Li, X. (2025). Rsar: Restricted state angle resolver and rotated sar benchmark. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA, 11\u201315 June 2025, IEEE.","DOI":"10.1109\/CVPR52734.2025.00695"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Xu, H., Liu, X., Xu, H., Ma, Y., Zhu, Z., Yan, C., and Dai, F. (2024). Rethinking boundary discontinuity problem for oriented object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 16\u201322 June 2024, IEEE.","DOI":"10.1109\/CVPR52733.2024.01648"},{"key":"ref_19","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, Montreal, QC, Canada, 11\u201317 October 2021, IEEE.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"3163","DOI":"10.1609\/aaai.v35i4.16426","article-title":"R3det: Refined single-stage detector with feature refinement for rotating object","volume":"Volume 35","author":"Yang","year":"2021","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"1938","DOI":"10.1109\/JSTARS.2021.3049851","article-title":"A novel CNN-based detector for ship detection based on rotatable bounding box in SAR images","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Lin, X., Zhang, B., Wu, F., Wang, C., Yang, Y., and Chen, H. (2023). SIVED: A SAR image dataset for vehicle detection based on rotatable bounding box. Remote Sens., 15.","DOI":"10.3390\/rs15112825"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1109\/LGRS.2005.845033","article-title":"A novel algorithm for ship detection in SAR imagery based on the wavelet transform","volume":"2","author":"Tello","year":"2005","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4510105","DOI":"10.1109\/LGRS.2022.3185118","article-title":"PolSAR image classification based on deep convolutional neural networks using wavelet transformation","volume":"19","author":"Jamali","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_26","first-page":"128430","article-title":"Sardet-100k: Towards open-source benchmark and toolkit for large-scale sar object detection","volume":"37","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yu, Y., and Da, F. (2023). Phase-shifting coder: Predicting accurate orientation in oriented object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada, 18\u201322 June 2023, IEEE.","DOI":"10.1109\/CVPR52729.2023.01283"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2458","DOI":"10.1609\/aaai.v35i3.16347","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"},{"key":"ref_29","first-page":"34899","article-title":"Fully convolutional one-stage 3d object detection on lidar range images","volume":"35","author":"Tian","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, Z., Wang, Y., Vaidya, S., Ruehle, F., Halverson, J., Solja\u010di\u0107, M., Hou, T.Y., and Tegmark, M. (2024). Kan: Kolmogorov-arnold networks. arXiv.","DOI":"10.1103\/4t7t-v19l"},{"key":"ref_31","first-page":"1","article-title":"A survey on kolmogorov-arnold network","volume":"58","author":"Somvanshi","year":"2025","journal-title":"ACM Comput. Surv."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1109\/TPAMI.2024.3508072","article-title":"STAR: A first-ever dataset and a large-scale benchmark for scene graph generation in large-size satellite imagery","volume":"47","author":"Li","year":"2025","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_33","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_34","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, Nashville, TN, USA, 19\u201325 June 2021, IEEE.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_35","first-page":"18381","article-title":"Learning high-precision bounding box for rotated object detection via kullback-leibler divergence","volume":"34","author":"Yang","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"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","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (2021). Rethinking rotated object detection with gaussian wasserstein distance loss. Proceedings of the International Conference on Machine Learning, JMLR."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhou, D., Fang, J., Song, X., Guan, C., Yin, J., Dai, Y., and Yang, R. (2019, January 16\u201319). Iou loss for 2d\/3d object detection. Proceedings of the 2019 International Conference on 3D Vision (3DV), Quebec City, QC, Canada.","DOI":"10.1109\/3DV.2019.00019"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Bu, L., Zhang, J., Zhang, Z., Yang, Y., and Deng, M. (2023). Enhancing RABASAR for Multi-Temporal SAR Image Despeckling through Directional Filtering and Wavelet Transform. Sensors, 23.","DOI":"10.3390\/s23218916"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"674","DOI":"10.1109\/34.192463","article-title":"A theory for multiresolution signal decomposition: The wavelet representation","volume":"11","author":"Mallat","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1773","DOI":"10.1109\/TGRS.2003.813488","article-title":"SAR image denoising via Bayesian wavelet shrinkage based on heavy-tailed modeling","volume":"41","author":"Achim","year":"2003","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"17941","DOI":"10.1038\/s41598-021-97195-6","article-title":"Research on improved convolutional wavelet neural network","volume":"11","author":"Liu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_43","first-page":"4009005","article-title":"Multilevel wavelet-SRNet for SAR target recognition","volume":"19","author":"Qin","year":"2021","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1331","DOI":"10.1002\/cpa.21413","article-title":"Group invariant scattering","volume":"65","author":"Mallat","year":"2012","journal-title":"Commun. Pure Appl. Math."},{"key":"ref_45","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, Long Beach, CA, USA, 15\u201320 June 2019, IEEE.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_46","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, Lisboa, Portugal.","DOI":"10.1145\/3503161.3548541"},{"key":"ref_47","first-page":"1922","article-title":"FCOS: A simple and strong anchor-free object detector","volume":"44","author":"Tian","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_48","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, Venice, Italy, 22\u201329 October 2017, IEEE.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_49","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_50","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_51","doi-asserted-by":"crossref","first-page":"113596","DOI":"10.1016\/j.engappai.2025.113596","article-title":"Rotation and semantic co-aware Transformer for oriented object detection in remote sensing images","volume":"166","author":"Lv","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_52","unstructured":"Gu, C., Chen, L., Gu, L., and Fu, Y. (2026). Fourier Angle Alignment for Oriented Object Detection in Remote Sensing. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Denver, CO, USA, 2\u20136 June 2026, IEEE."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1007\/s11063-025-11782-6","article-title":"MSFKAN: A Multi-Scale Feature Prediction Network Combined with KAN for Medical Image Classification","volume":"57","author":"Gao","year":"2025","journal-title":"Neural Process. Lett."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Li, C., Liu, X., Li, W., Wang, C., Liu, H., Liu, Y., Chen, Z., and Yuan, Y. (2025). U-kan makes strong backbone for medical image segmentation and generation. Proceedings of the AAAI Conference on Artificial Intelligence, AAAI Press.","DOI":"10.1609\/aaai.v39i5.32491"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1007\/s00530-026-02277-y","article-title":"A hybrid Kolmogorov-Arnold network for medical image segmentation","volume":"32","author":"Bhattacharyya","year":"2026","journal-title":"Multimed. Syst."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Ding, Z., Hou, R., Men, Y., Luan, S., Liu, Y., He, K., and Xie, S. (2025). DSKFuse: Passive-Active Distillation Learning for Multi-modal Image Fusion via Dynamic Sparse Kansformer. Expert Syst. Appl., 130610.","DOI":"10.2139\/ssrn.5294957"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_58","first-page":"1","article-title":"Kymatio: Scattering transforms in python","volume":"21","author":"Andreux","year":"2020","journal-title":"J. Mach. Learn. Res."},{"key":"ref_59","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, Las Vegas, NV, USA, 27\u201330 June 2016, IEEE.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_60","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, Honolulu, HI, USA, 21\u201326 July 2017, IEEE.","DOI":"10.1109\/CVPR.2017.106"},{"key":"ref_61","unstructured":"Zhu, X., Su, W., Lu, L., Li, B., Wang, X., and Dai, J. (2020). Deformable detr: Deformable transformers for end-to-end object detection. arXiv."},{"key":"ref_62","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, Nashville, TN, USA, 19\u201325 June 2021, IEEE.","DOI":"10.1109\/CVPR46437.2021.00868"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2011\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T13:08:16Z","timestamp":1774357696000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/7\/2011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,24]]},"references-count":62,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["s26072011"],"URL":"https:\/\/doi.org\/10.3390\/s26072011","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,24]]}}}