{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T05:02:33Z","timestamp":1790139753508,"version":"4.0.1"},"reference-count":34,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T00:00:00Z","timestamp":1789344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62273243"],"award-info":[{"award-number":["62273243"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"crossref"}]},{"award":["62273243"],"award-info":[{"award-number":["62273243"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Science and Technology Program of Liaoning Province","award":["2025JH2\/102800064"],"award-info":[{"award-number":["2025JH2\/102800064"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV) imagery is a core data source for remote sensing interpretation, intelligent transportation, urban monitoring, and disaster assessment, yet its large scale variation, dense object distributions, and complex backgrounds continue to challenge automated detection systems. Transformer-based detectors offer strong global modeling capacity, but existing implementations still suffer from insufficient multi-scale feature interaction, weak discriminative representation, and loss of fine-grained spatial detail, which together limit performance on small and densely arranged targets. This paper proposes MSF-DETR, a multi-scale feature enhancement framework built on RT-DETR that integrates four coordinated components: an Enhanced Feature Connection (EFC) module for adaptive cross-scale interaction, a Feature Channel Attention (FCA) module for frequency-domain discriminative enhancement, a Reinforced Attention Feedback Module (RAFM) for spatial-detail preservation within the Transformer encoder, and a Unified Query Supervision Loss (UQSL) for stable dense-scene supervision. On the DIOR benchmark, MSF-DETR achieves 86.3% mAP50 and 64.4% mAP50\u201395, improving on the RT-DETR baseline by 2.8 and 2.6 percentage points, respectively; on DOTA, it reaches 77.2% mAP50 and 48.8% mAP50\u201395, improvements of 4.7 and 4.6 points. These results demonstrate that jointly coordinating multi-scale fusion, channel discrimination, spatial-detail retention, and query-level supervision, rather than stacking independent modules, yields measurable robustness gains for small and densely distributed objects in UAV aerial imagery.<\/jats:p>","DOI":"10.3390\/s26185832","type":"journal-article","created":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:19:04Z","timestamp":1789399144000},"page":"5832","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Bridging the Scale Gap: A Multi-Scale Feature Enhancement Framework for UAV Aerial Image Object Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2495-9530","authenticated-orcid":false,"given":"Dan","family":"Shan","sequence":"first","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zanqi","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dadi","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4078-7018","authenticated-orcid":false,"given":"Carlos","family":"Ferran","sequence":"additional","affiliation":[{"name":"College of Business, Governors State University, University Park, IL 60484, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuan","family":"Tong","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6382-3168","authenticated-orcid":false,"given":"Dongming","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electrical and Control Engineering, Shenyang Jianzhu University, Shenyang 110168, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, IEEE.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1680","DOI":"10.3390\/make5040083","article-title":"A comprehensive review of YOLO architectures in computer vision: From YOLOv1 to YOLOv8 and YOLO-NAS","volume":"5","author":"Terven","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Tang, G., Ni, J., Zhao, Y., Gu, Y., and Cao, W. (2023). A survey of object detection for UAVs based on deep learning. Remote Sens., 16.","DOI":"10.3390\/rs16010149"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1758","DOI":"10.1109\/TCSVT.2019.2905881","article-title":"Small object detection in unmanned aerial vehicle images using feature fusion and scaling-based single shot detector with spatial context analysis","volume":"30","author":"Liang","year":"2019","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Tan, M., Pang, R., and Le, Q.V. (2020). Efficientdet: Scalable and efficient object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Zhang, G., Peng, Y., and Li, J. (2025). YOLO-MARS: An enhanced YOLOV8N for small object detection in UAV aerial imagery. Sensors, 25.","DOI":"10.3390\/s25082534"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s44196-024-00632-3","article-title":"Small Object Detection in UAV Images Based on YOLOv8n","volume":"17","author":"Xu","year":"2024","journal-title":"Int. J. Comput. Intell. Syst."},{"key":"ref_9","unstructured":"Chen, D., Hu, Y., and Zhang, L. (2025). HierLight-YOLO: A Hierarchical and Lightweight Object Detection Network for UAV Photography. arXiv."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Lu, L., He, D., Deng, Z., Liu, C., and Shen, Z. (2025). MASF-YOLO: An improved YOLOv11 network for small object detection on drone view. 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC), IEEE.","DOI":"10.1109\/SMC58881.2025.11342487"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhai, X., Huang, Z., Li, T., Liu, H., and Wang, S. (2023). YOLO-Drone: An optimized YOLOv8 network for tiny UAV object detection. Electronics, 12.","DOI":"10.3390\/electronics12173664"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1109\/TPAMI.2022.3152247","article-title":"A survey on vision transformer","volume":"45","author":"Han","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_13","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. European Conference on Computer Vision, Springer International Publishing.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_14","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_15","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Lv, W., Xu, S., Wei, J., Wang, G., Dang, Q., Liu, Y., and Chen, J. (2024). Detrs beat yolos on real-time object detection. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012085","DOI":"10.1088\/1742-6596\/1827\/1\/012085","article-title":"Analysis of object detection performance based on Faster R-CNN","volume":"1827","author":"Li","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Li, F., Zeng, A., Liu, S., Zhang, H., Li, H., Zhang, L., and Ni, L.M. (2023). Lite detr: An interleaved multi-scale encoder for efficient detr. 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE.","DOI":"10.1109\/CVPR52729.2023.01780"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"107563","DOI":"10.1016\/j.compag.2022.107563","article-title":"Faster and accurate green pepper detection using NSGA-II-based pruned YOLOv5l in the field environment","volume":"205","author":"Nan","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Cheng, P., Tang, X., Liang, W., Li, Y., Cong, W., and Zang, C. (2023). Tiny-YOLOv7: Tiny object detection model for drone imagery. International Conference on Image and Graphics, Springer Nature.","DOI":"10.1007\/978-3-031-46311-2_5"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"20939","DOI":"10.1007\/s00521-023-08809-1","article-title":"An improved fire detection approach based on YOLO-v8 for smart cities","volume":"35","author":"Talaat","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, P., Liao, X., Liu, C., He, X., Zhao, K., and Hu, D. (Sci. Rep., 2026). A method for recognizing and detecting the physiological state of silkworms based on an improved YOLOv8 approach, Sci. Rep., ahead of print.","DOI":"10.1038\/s41598-026-62714-w"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Makhmudov, F., Zohirov, K., Kuvandikov, J., Temirov, Z., Bobomirzayevich, A.A., Mukhiddinov, M., Muraeva, K., Sevinov, J., and Bolikulov, F. (2026). YOLOv9-Based Detection of Diseases in Poplar Trees Using Histogram Equalization and Computer Vision. Sensors, 26.","DOI":"10.3390\/s26113320"},{"key":"ref_23","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_24","doi-asserted-by":"crossref","unstructured":"Jegham, N., Koh, C.Y., Abdelatti, M., and Hendawi, A. (2024). Yolo evolution: A comprehensive benchmark and architectural review of yolov12, yolo11, and their previous versions. arXiv.","DOI":"10.2139\/ssrn.5175639"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1007\/s11554-024-01550-5","article-title":"LGFF-YOLO: Small object detection method of UAV images based on efficient local\u2013global feature fusion","volume":"21","author":"Peng","year":"2024","journal-title":"J. Real-Time Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wei, C., and Wang, W. (2025). Rfag-yolo: A receptive field attention-guided yolo network for small-object detection in uav images. Sensors, 25.","DOI":"10.3390\/s25072193"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Li, H., and Qu, H. (2025). Dassf: Dynamic-attention scale-sequence fusion for aerial object detection. International Conference on Computational Visual Media, Springer Nature.","DOI":"10.1007\/978-981-96-5809-1_12"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Li, Q., and Shi, H. (2024). Yolo-ge: An attention fusion enhanced underwater object detection algorithm. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12101885"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Lu, Y., and Sun, M. (2024). SSE-YOLO: Efficient UAV Target Detection with Less Parameters and High Accuracy. Preprints.","DOI":"10.20944\/preprints202401.1108.v1"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Liu, B., Han, Y., Liu, P., Luo, S., Li, J., Zhang, T., and Cui, W. (2025). YOLO-MARS for Infrared Target Detection: Towards near Space. Sensors, 25.","DOI":"10.3390\/s25175538"},{"key":"ref_31","unstructured":"Liu, S., Li, F., Zhang, H., Yang, X., Qi, X., Su, H., Zhu, J., and Zhang, L. (2022). Dab-detr: Dynamic anchor boxes are better queries for detr. arXiv."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chen, Q., Chen, X., Wang, J., Zhang, S., Yao, K., Feng, H., Han, J., Ding, E., Zeng, G., and Wang, J. (2023). Group detr: Fast detr training with group-wise one-to-many assignment. 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.00610"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zong, Z., Song, G., and Liu, Y. (2023). Detrs with collaborative hybrid assignments training. 2023 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV51070.2023.00621"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, W., Zhang, Y., Xu, T., An, T., Liang, Y., and Zhang, B. (2025). Object detection for medical image analysis: Insights from the RT-DETR model. Proceedings of the 2025 International Conference on Artificial Intelligence and Computational Intelligence, Springer.","DOI":"10.1145\/3730436.3730506"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5832\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T04:12:23Z","timestamp":1790136743000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/18\/5832"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,9,14]]},"references-count":34,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2026,9]]}},"alternative-id":["s26185832"],"URL":"https:\/\/doi.org\/10.3390\/s26185832","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,9,14]]}}}