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To address these coupled difficulties, we propose EDM-Net, an end-to-end multi-scale detector that organizes feature processing into three coordinated stages: adaptive extraction, intra-scale interaction, and cross-scale fusion. First, an efficient sparse mixture-of-experts (ES-MoE) module is embedded in the backbone to allocate scale-specific convolutional experts according to scene-level feature responses, providing a more adaptive feature basis than a single static extraction path. Second, a dynamic mixing intra-scale feature interaction (DMIFI) module is introduced into the Transformer encoder. This module combines global self-attention with dynamic spatial mixing, thereby preserving long-range context while reintroducing local two-dimensional inductive bias for dense and small objects. Third, a multi-scale synergistic attention fusion (MSAF) module aligns adjacent feature levels through parallel local and global attention branches and structural re-parameterization, reducing semantic dilution during feature aggregation. Comprehensive experiments on three large-scale remote sensing benchmark datasets, DIOR, NWPU VHR-10, and RSOD, demonstrate that EDM-Net consistently improves over the re-trained RT-DETR-R18 baseline under the same experimental protocol, attaining mAP50 scores of 83.7%, 95.6%, and 95.8% respectively. Additional ablation and scale-specific analyses indicate that the three modules contribute complementary gains, especially for small and densely distributed objects. These results suggest that coordinated extraction, interaction, and fusion can improve remote sensing object detection under complex scale and background conditions.<\/jats:p>","DOI":"10.3390\/s26123927","type":"journal-article","created":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T01:03:23Z","timestamp":1782263003000},"page":"3927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["EDM-Net: A Multi-Scale Network for Object Detection in Remote Sensing Images"],"prefix":"10.3390","volume":"26","author":[{"given":"Shuai","family":"Liang","sequence":"first","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222000, China"},{"name":"Key Laboratory of Marine Meteorological Disaster Prevention and Mitigation of Jiang Su Province, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5348-7144","authenticated-orcid":false,"given":"Xiao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222000, China"},{"name":"Key Laboratory of Marine Meteorological Disaster Prevention and Mitigation of Jiang Su Province, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222000, China"},{"name":"Key Laboratory of Marine Meteorological Disaster Prevention and Mitigation of Jiang Su Province, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5670-0645","authenticated-orcid":false,"given":"Hui","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222000, China"},{"name":"Key Laboratory of Marine Meteorological Disaster Prevention and Mitigation of Jiang Su Province, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huilei","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Marine Technology and Geomatics, Jiangsu Ocean University, Lianyungang 222000, China"},{"name":"Key Laboratory of Marine Meteorological Disaster Prevention and Mitigation of Jiang Su Province, Lianyungang 222005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.isprsjprs.2016.03.014","article-title":"A survey on object detection in optical remote sensing images","volume":"117","author":"Cheng","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1109\/MGRS.2017.2762307","article-title":"Deep learning in remote sensing: A comprehensive review and list of resources","volume":"5","author":"Zhu","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Liu, S., Zou, H., Huang, Y., Cao, X., He, S., Li, M., and Zhang, Y. (2023). ERF-RTMDet: An improved small object detection method in remote sensing images. Remote Sens., 15.","DOI":"10.3390\/rs15235575"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"119960","DOI":"10.1016\/j.eswa.2023.119960","article-title":"A comprehensive survey of oriented object detection in remote sensing images","volume":"224","author":"Wen","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109466","DOI":"10.1016\/j.patcog.2023.109466","article-title":"Adversarial pan-sharpening attacks for object detection in remote sensing","volume":"139","author":"Wei","year":"2023","journal-title":"Pattern Recognit."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TGRS.2021.3064840","article-title":"Global to local: Clip-LSTM-based object detection from remote sensing images","volume":"60","author":"Teng","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_7","first-page":"5616217","article-title":"Feature split\u2013merge\u2013enhancement network for remote sensing object detection","volume":"60","author":"Ma","year":"2022","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","first-page":"5600617","article-title":"CoF-Net: A progressive coarse-to-fine framework for object detection in remote-sensing imagery","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.isprsjprs.2018.04.003","article-title":"Multi-scale object detection in remote sensing imagery with convolutional neural networks","volume":"145","author":"Deng","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"10015","DOI":"10.1109\/TGRS.2019.2930982","article-title":"CAD-Net: A context-aware detection network for objects in remote sensing imagery","volume":"57","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","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\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3377","DOI":"10.1109\/TGRS.2019.2954328","article-title":"FMSSD: Feature-merged single-shot detection for multiscale objects in large-scale remote sensing imagery","volume":"58","author":"Wang","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"111260","DOI":"10.1016\/j.compag.2025.111260","article-title":"CitrusNet: A vision transformer-CNN approach for citrus detection from multi-source imagery with multi-scale feature integration","volume":"241","author":"Wang","year":"2026","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2337","DOI":"10.1109\/TGRS.2017.2778300","article-title":"Rotation-insensitive and context-augmented object detection in remote sensing images","volume":"56","author":"Li","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_15","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":"2021","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Xu, X., Dong, S., Xu, T., Ding, L., Wang, J., Jiang, P., Song, L., and Li, J. (2023). Fusionrcnn: Lidar-camera fusion for two-stage 3d object detection. Remote Sens., 15.","DOI":"10.3390\/rs15071839"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5531015","DOI":"10.1109\/TGRS.2023.3332179","article-title":"Modality registration and object search framework for UAV-based unregistered RGB-T image salient object detection","volume":"61","author":"Song","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6514505","DOI":"10.1109\/LGRS.2022.3197617","article-title":"Rotated object detection via scale-invariant mahalanobis distance in aerial images","volume":"19","author":"Wen","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_19","first-page":"91","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_20","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018, January 18\u201322). Cascade r-cnn: Delving into high quality object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sun, P., Zhang, R., Jiang, Y., Kong, T., Xu, C., Zhan, W., Tomizuka, M., Li, L., Yuan, Z., and Wang, C. (2021, January 19\u201325). Sparse r-cnn: End-to-end object detection with learnable proposals. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Virtual.","DOI":"10.1109\/CVPR46437.2021.01422"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_23","unstructured":"Wang, C.-Y., Yeh, I.-H., and Mark Liao, H.-Y. (October, January 29). Yolov9: Learning what you want to learn using programmable gradient information. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"107984","DOI":"10.52202\/079017-3429","article-title":"Yolov10: Real-time end-to-end object detection","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","unstructured":"Khanam, R., and Hussain, M. (2024). Yolov11: An overview of the key architectural enhancements. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"2405525","DOI":"10.1080\/17538947.2024.2405525","article-title":"YOLOSAR-Lite: A lightweight framework for real-time ship detection in SAR imagery","volume":"17","author":"Wang","year":"2024","journal-title":"Int. J. Digit. Earth"},{"key":"ref_27","unstructured":"Sapkota, R., Cheppally, R.H., Sharda, A., and Karkee, M. (2025). YOLO26: Key architectural enhancements and performance benchmarking for real-time object detection. arXiv."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016, January 11\u201314). Ssd: Single shot multibox detector. Proceedings of the European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_29","unstructured":"Li, Z., Yang, L., and Zhou, F. (2017). FSSD: Feature fusion single shot multibox detector. arXiv."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., and Zagoruyko, S. (2020, January 23\u201328). End-to-end object detection with transformers. Proceedings of the European Conference on Computer Vision, Glasgow, UK.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_31","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_32","unstructured":"Zhang, H., Li, F., Liu, S., Zhang, L., Su, H., Zhu, J., Ni, L.M., and Shum, H.-Y. (2022). Dino: Detr with improved denoising anchor boxes for end-to-end object detection. arXiv."},{"key":"ref_33","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_34","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_35","doi-asserted-by":"crossref","first-page":"2486","DOI":"10.1109\/TGRS.2016.2645610","article-title":"Accurate object localization in remote sensing images based on convolutional neural networks","volume":"55","author":"Long","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","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, January 12). Microsoft coco: Common objects in context. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, W., Guo, H., Zhang, R., and Xia, G.-S. (2021, January 10\u201315). Tiny object detection in aerial images. Proceedings of the 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy.","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"ref_38","first-page":"1","article-title":"CDD-Net: A context-driven detection network for multiclass object detection","volume":"19","author":"Wu","year":"2020","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_39","first-page":"102966","article-title":"A context-scale-aware detector and a new benchmark for remote sensing small weak object detection in unmanned aerial vehicle images","volume":"112","author":"Han","year":"2022","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1968","DOI":"10.1109\/TMM.2021.3074273","article-title":"Extended feature pyramid network for small object detection","volume":"24","author":"Deng","year":"2021","journal-title":"IEEE Trans. Multimed."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"110983","DOI":"10.1016\/j.patcog.2024.110983","article-title":"Msnet: Multi-scale network for object detection in remote sensing images","volume":"158","author":"Gao","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_42","first-page":"4701013","article-title":"MDADet: A Multimodal Dynamic Adaptation Framework for Efficient Small Object Detection in Aerial Images","volume":"64","author":"Zhang","year":"2026","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_43","first-page":"4406410","article-title":"SFSANet: Multiscale Object Detection in Remote Sensing Image Based on Semantic Fusion and Scale Adaptability","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"5635216","DOI":"10.1109\/TGRS.2025.3591260","article-title":"Multiscale Gaussian Attention Mechanism for Tiny Object Detection in Remote Sensing Images","volume":"63","author":"Shi","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"15376","DOI":"10.1109\/JSTARS.2025.3575239","article-title":"LGC-YOLO: Local-Global Feature Extraction and Coordination Network With Contextual Interaction for Remote Sensing Object Detection","volume":"18","author":"Wu","year":"2025","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Doll\u00e1r, P., Girshick, R., He, K., Hariharan, B., and Belongie, S. (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_47","doi-asserted-by":"crossref","unstructured":"Ghiasi, G., Lin, T.-Y., and Le, Q.V. (2019, January 15\u201320). Nas-fpn: Learning scalable feature pyramid architecture for object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA.","DOI":"10.1109\/CVPR.2019.00720"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020, January 13\u201319). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Liu, S., Qi, L., Qin, H., Shi, J., and Jia, J. (2018, January 18\u201323). Path aggregation network for instance segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_50","first-page":"5610013","article-title":"Foreground refinement network for rotated object detection in remote sensing images","volume":"60","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024, January 16\u201322). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Huang, S., Lu, Z., Cun, X., Yu, Y., Zhou, X., and Shen, X. (2025, January 11\u201315). Deim: Detr with improved matching for fast convergence. Proceedings of the Computer Vision and Pattern Recognition Conference, Nashville, TN, USA.","DOI":"10.1109\/CVPR52734.2025.01412"},{"key":"ref_53","first-page":"5641214","article-title":"Position-DETR: Step-by-step position-guided small object detection in remote sensing images","volume":"63","author":"Zhao","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"5646415","DOI":"10.1109\/TGRS.2025.3618293","article-title":"SAFF-DETR: An End-to-End Object Detection Network for Remote Sensing Images With Targets of Varying Sizes Based on Scale Adaptation and Frequency Fusion","volume":"63","author":"Zhi","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_55","first-page":"4421520","article-title":"A Scale-Aware Multi-Domain DETR for Small Object Detection in UAV Remote Sensing Imagery","volume":"63","author":"Li","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_56","unstructured":"Lin, X., Peng, J., Gan, Z., Zhu, J., and Liu, J. (2025). YOLO-Master: MOE-Accelerated with Specialized Transformers for Enhanced Real-time Detection. arXiv."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1162\/neco.1991.3.1.79","article-title":"Adaptive mixtures of local experts","volume":"3","author":"Jacobs","year":"1991","journal-title":"Neural Comput."},{"key":"ref_58","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (November, January 27). Fcos: Fully convolutional one-stage object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Republic of Korea."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Zhang, P., Liu, J., Zhang, J., Liu, Y., and Shi, J. (2025). HAF-YOLO: Dynamic feature aggregation network for object detection in remote-sensing images. Remote Sens., 17.","DOI":"10.3390\/rs17152708"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Yuan, H., Zhang, B., Wang, Y., and Qiang, Q. (2026). From Structural Degradation to Semantic Misalignment: A Unified Frequency-Aware Compensation Framework for Remote Sensing Object Detection. Remote Sens., 18.","DOI":"10.3390\/rs18050777"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yang, F., Chen, G., and Duan, J. (2024). Skip-Encoder and Skip-Decoder for Detection Transformer in Optical Remote Sensing. Remote Sens., 16.","DOI":"10.3390\/rs16162884"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"110976","DOI":"10.1016\/j.patcog.2024.110976","article-title":"TBNet: A texture and boundary-aware network for small weak object detection in remote-sensing imagery","volume":"158","author":"Li","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Li, Y., Hou, Q., Zheng, Z., Cheng, M.-M., Yang, J., and Li, X. (2023, January 2\u20136). Large selective kernel network for remote sensing object detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Paris, France.","DOI":"10.1109\/ICCV51070.2023.01540"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.isprsjprs.2026.01.017","article-title":"Knowledge distillation with spatial semantic enhancement for remote sensing object detection","volume":"233","author":"Hu","year":"2026","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_65","first-page":"8534","article-title":"Background suppression network with attention collapse inhibited transformer for optical remote sensing object detection","volume":"63","author":"Li","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"8534","DOI":"10.1109\/TGRS.2019.2921396","article-title":"Sig-NMS-based faster R-CNN combining transfer learning for small target detection in VHR optical remote sensing imagery","volume":"57","author":"Dong","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_67","first-page":"5603613","article-title":"CANet: Centerness-aware network for object detection in remote sensing images","volume":"60","author":"Shi","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ma, J., Wang, G., Yin, R., He, G., Zhou, D., Long, T., Adam, E., and Zhang, Z. (2026). Wind Turbines Small Object Detection in Remote Sensing Images Based on CGA-YOLO: A Case Study in Shandong Province, China. Remote Sens., 18.","DOI":"10.3390\/rs18020324"},{"key":"ref_69","first-page":"5603214","article-title":"Attention-free global multiscale fusion network for remote sensing object detection","volume":"62","author":"Gao","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Wei, Y., Zhou, X., Guo, Y., Chen, J., and Jiang, T. (2025). YOLO-SBA: A multi-scale and complex background aware framework for remote sensing target detection. Remote Sens., 17.","DOI":"10.3390\/rs17121989"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3927\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T04:28:31Z","timestamp":1782534511000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/12\/3927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,20]]},"references-count":70,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2026,6]]}},"alternative-id":["s26123927"],"URL":"https:\/\/doi.org\/10.3390\/s26123927","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,20]]}}}