{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T09:07:46Z","timestamp":1765357666064,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,18]],"date-time":"2024-08-18T00:00:00Z","timestamp":1723939200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In optical remote sensing image object detection, discontinuous boundaries often limit detection accuracy, particularly at high Intersection over Union (IoU) thresholds. This paper addresses this issue by proposing the Spatial Adaptive Angle-Aware (SA3) Network. The SA3 Network employs a hierarchical refinement approach, consisting of coarse regression, fine regression, and precise tuning, to optimize the angle parameters of rotated bounding boxes. It adapts to specific task scenarios using either class-aware or class-agnostic strategies. Experimental results demonstrate its effectiveness in significantly improving detection accuracy at high IoU thresholds. Additionally, we introduce a Gaussian transform-based IoU factor during angle regression loss calculation, leading to the development of Edge-aware Skewed Bounding Box Loss (EAS Loss). The EAS loss enhances the loss gradient at the final stage of angle regression for bounding boxes, addressing the challenge of further learning when the predicted box angle closely aligns with the real target box angle. This results in increased training efficiency and better alignment between training and evaluation metrics. Experimental results show that the proposed method substantially enhances the detection accuracy of ReDet and ReBiDet models. The SA3 Network and EAS loss not only elevate the mAP of the ReBiDet model on DOTA-v1.5 to 78.85% but also effectively improve the model\u2019s mAP under high IoU threshold conditions.<\/jats:p>","DOI":"10.3390\/s24165342","type":"journal-article","created":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T06:41:31Z","timestamp":1724049691000},"page":"5342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Elevating Detection Performance in Optical Remote Sensing Image Object Detection: A Dual Strategy with Spatially Adaptive Angle-Aware Networks and Edge-Aware Skewed Bounding Box Loss Function"],"prefix":"10.3390","volume":"24","author":[{"given":"Zexin","family":"Yan","sequence":"first","affiliation":[{"name":"Institute of System Engineering, Academy of Military Sciences, Beijing 100141, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jie","family":"Fan","sequence":"additional","affiliation":[{"name":"Institute of System Engineering, Academy of Military Sciences, Beijing 100141, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhongbo","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of System Engineering, Academy of Military Sciences, Beijing 100141, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Xie","sequence":"additional","affiliation":[{"name":"Institute of System Engineering, Academy of Military Sciences, Beijing 100141, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,18]]},"reference":[{"key":"ref_1","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_2","doi-asserted-by":"crossref","first-page":"3676","DOI":"10.1109\/TIP.2018.2825107","article-title":"TextBoxes++: A Single-Shot Oriented Scene Text Detector","volume":"27","author":"Liao","year":"2018","journal-title":"IEEE Trans. Image Process."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liao, M., Zhu, Z., Shi, B., Xia, G.s., and Bai, X. (2018, January 18\u201323). Rotation-Sensitive Regression for Oriented Scene Text Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00619"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Liu, X., Liang, D., Yan, S., Chen, D., Qiao, Y., and Yan, J. (2018, January 18\u201323). FOTS: Fast Oriented Text Spotting with a Unified Network. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00595"},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Zhou, X., Yao, C., Wen, H., Wang, Y., Zhou, S., He, W., and Liang, J. (2017, January 21\u201326). EAST: An Efficient and Accurate Scene Text Detector. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.283"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TPAMI.2007.1011","article-title":"High-Performance Rotation Invariant Multiview Face Detection","volume":"29","author":"Huang","year":"2007","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Rowley, H.A., Baluja, S., and Kanade, T. (1998, January 25). Rotation invariant neural network-based face detection. Proceedings of the 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), Santa Barbara, CA, USA."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Zhou, L.F., Gu, Y., Wang, P.S.P., Liu, F.Y., Liu, J., and Xu, T.Y. (2020). Rotation-Invariant Face Detection with Multi-task Progressive Calibration Networks. Pattern Recognition and Artificial Intelligence, Springer International Publishing.","DOI":"10.1007\/978-3-030-59830-3_44"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Azimi, S.M., Vig, E., Bahmanyar, R., K\u00f6rner, M., and Reinartz, P. (2019). Towards Multi-class Object Detection in Unconstrained Remote Sensing Imagery. Lecture Notes in Computer Science, Springer.","DOI":"10.1007\/978-3-030-20893-6_10"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Ding, J., Xue, N., Long, Y., Xia, G., and Lu, Q. (2019, January 15\u201320). Learning RoI Transformer for Oriented Object Detection in Aerial Images. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00296"},{"key":"ref_12","first-page":"3163","article-title":"R3Det: Refined Single-Stage Detector with Feature Refinement for Rotating Object","volume":"35","author":"Yang","year":"2021","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_13","doi-asserted-by":"crossref","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 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), Seoul, Republic of Korea.","DOI":"10.1109\/ICCV.2019.00832"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Yang, X., Sun, H., Fu, K., Yang, J., Sun, X., Yan, M., and Guo, Z. (2018). Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks. Remote. Sens., 10.","DOI":"10.3390\/rs10010132"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2384","DOI":"10.1109\/TPAMI.2022.3166956","article-title":"SCRDet++: Detecting Small, Cluttered and Rotated Objects via Instance-Level Feature Denoising and Rotation Loss Smoothing","volume":"45","author":"Yang","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Liu, Y., Zhang, S., Jin, L., Xie, L., Wu, Y., and Wang, Z. (2019, January 10\u201316). Omnidirectional scene text detection with sequential-free box discretization. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, China.","DOI":"10.24963\/ijcai.2019\/423"},{"key":"ref_17","first-page":"2458","article-title":"Learning Modulated Loss for Rotated Object Detection","volume":"35","author":"Qian","year":"2021","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_18","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_19","first-page":"4335","article-title":"Detecting Rotated Objects as Gaussian Distributions and its 3-D Generalization","volume":"45","author":"Yang","year":"2023","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Xie, X., Cheng, G., Wang, J., Yao, X., and Han, J. (2021, January 10\u201317). Oriented R-CNN for Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00350"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, J., Ding, J., Xue, N., and Xia, G. (2021, January 20\u201325). ReDet: A Rotation-equivariant Detector for Aerial Object Detection. Proceedings of the 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00281"},{"key":"ref_24","first-page":"2355","article-title":"Dynamic Anchor Learning for Arbitrary-Oriented Object Detection","volume":"35","author":"Ming","year":"2021","journal-title":"Proc. Aaai Conf. Artif. Intell."},{"key":"ref_25","first-page":"1","article-title":"Align Deep Features for Oriented Object Detection","volume":"60","author":"Han","year":"2022","journal-title":"IEEE Trans. Geosci. Remote. Sens."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1873","DOI":"10.1007\/s11263-022-01618-4","article-title":"On the Arbitrary-Oriented Object Detection: Classification Based Approaches Revisited","volume":"130","author":"Yang","year":"2022","journal-title":"Int. J. Comput. Vis."},{"key":"ref_27","unstructured":"Weiler, M., and Cesa, G. (2019). General E(2)-Equivariant Steerable CNNs. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yan, Z.X., Li, Z.B., Xie, Y.Q., Li, C.Y., Li, S.A., and Sun, F.W. (2023). ReBiDet: An Enhanced Ship Detection Model Utilizing ReDet and Bi-Directional Feature Fusion. Appl. Sci., 13.","DOI":"10.3390\/app13127080"},{"key":"ref_29","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\u201320). Hybrid Task Cascade for Instance Segmentation. Proceedings of the 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00511"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","article-title":"Mask R-CNN","volume":"42","author":"He","year":"2020","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Jaiswal, A., Wu, Y., Natarajan, P., and Natarajan, P. (2021, January 3\u20138). Class-agnostic Object Detection. Proceedings of the 2021 IEEE Winter Conference on Applications of Computer Vision (WACV), Waikoloa, HI, USA.","DOI":"10.1109\/WACV48630.2021.00096"},{"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 Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58558-7_12"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zheng, Y., Zhang, D., Xie, S., Lu, J., and Zhou, J. (2020, January 23\u201328). Rotation-Robust Intersection over Union for 3D Object Detection. Proceedings of the Computer Vision\u2014ECCV 2020, Glasgow, UK.","DOI":"10.1007\/978-3-030-58565-5_28"},{"key":"ref_34","unstructured":"Yang, X., Yan, J., Ming, Q., Wang, W., Zhang, X., and Tian, Q. (July, January 18\u2013). Rethinking Rotated Object Detection with Gaussian Wasserstein Distance Loss. Proceedings of the 2021 International Conference on Machine Learning, Virtual."},{"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","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, January 18\u201323). DOTA: A Large-Scale Dataset for Object Detection in Aerial Images. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00418"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"7778","DOI":"10.1109\/TPAMI.2021.3117983","article-title":"Object Detection in Aerial Images: A Large-Scale Benchmark and Challenges","volume":"44","author":"Ding","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_39","unstructured":"Data, B., and Laboratory, D.M.N. (2024, July 31). Fine-Grained Dense Ship Detection Task Based on High-Resolution Remote Sensing Visible Light Data. Available online: https:\/\/www.datafountain.cn\/competitions\/635\/datasets."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal Loss for Dense Object Detection. Proceedings of the 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_41","unstructured":"Lang, S., Ventola, F.G., and Kersting, K. (2021). DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection. arXiv."},{"key":"ref_42","unstructured":"Li, C., Xu, C., Cui, Z., Wang, D., Jie, Z., Zhang, T., and Yang, J. (2019, January 15\u201320). Learning Object-Wise Semantic Representation for Detection in Remote Sensing Imagery. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, Long Beach, CA, USA."},{"key":"ref_43","unstructured":"Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., and Chen, K. (2022). RTMDet: An Empirical Study of Designing Real-Time Object Detectors. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5342\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:38:29Z","timestamp":1760110709000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/16\/5342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,18]]},"references-count":43,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["s24165342"],"URL":"https:\/\/doi.org\/10.3390\/s24165342","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,8,18]]}}}