{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T22:18:24Z","timestamp":1788387504872,"version":"build-2803163510"},"reference-count":49,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2026,5,6]],"date-time":"2026-05-06T00:00:00Z","timestamp":1778025600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009110","name":"Natural Science Foundation of Xinjiang Uygur Autonomous Region","doi-asserted-by":"publisher","award":["2025D01C27"],"award-info":[{"award-number":["2025D01C27"]}],"id":[{"id":"10.13039\/100009110","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42501071"],"award-info":[{"award-number":["42501071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["42501071"],"award-info":[{"award-number":["42501071"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"name":"Tianchi Talent Introduction Programme"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Object detection in unmanned aerial vehicle (UAV) remote sensing imagery remains a formidable challenge due to the diminutive scale of targets, complex background clutter, and extreme variability in target morphology. Standard convolutional neural networks typically suffer from irreversible fine-grained information loss during downsampling, as strided operations discard critical spatial details essential for the localization of tiny objects. To address these issues, we propose RFE-YOLO, a lightweight receptive field-enhanced network specifically tailored for high-precision small object detection in UAV scenarios. First, the Cross-Scale Receptive Field Enhancement (CSRE) module is designed to mitigate intrinsic information loss by integrating space-to-depth convolution (SPD-Conv), which preserves spatial details by migrating them into the channel dimension. This module further employs an energy-based adaptive weight generation mechanism to distinguish target signals from environmental noise. Second, this paper proposes the C3k2-Dynamic Inception Mixer Block (C3k2-DIMB), which adaptively captures anisotropic features\u2014such as slender vehicles\u2014via dynamic kernel weighting and multi-shape inception kernels. Third, the Shuffled Upsampling for Resolution Enhancement (SURE) module is introduced to maintain spatial fidelity during resolution recovery, utilizing a channel shuffle mechanism to overcome information isolation. Finally, the Multi-feature Fusion Module (MFM) replaces conventional static concatenation with a dynamic softmax-based competition mechanism, effectively bridging the semantic gap between multi-level features while suppressing background distractors. Experimental results on the VisDrone dataset demonstrate that RFE-YOLO significantly enhances the representation capability for small objects. Specifically, the proposed model achieves a state-of-the-art mAP50 of 42.70%, representing a substantial 9.3% improvement over the baseline YOLO11n. Furthermore, our architecture maintains an exceptionally lightweight profile with only 1.91 M parameters, demonstrating that high-precision detection can be achieved through structural intelligence rather than excessive parameter scaling. This makes RFE-YOLO highly suitable for real-time inference on edge-deployed UAV platforms.<\/jats:p>","DOI":"10.3390\/s26092903","type":"journal-article","created":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:03:37Z","timestamp":1778144617000},"page":"2903","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["RFE-YOLO: A Lightweight Receptive Field-Enhanced Network for UAV Imagery Object Detection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-8293-1488","authenticated-orcid":false,"given":"Yimo","family":"Peng","sequence":"first","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7855-6525","authenticated-orcid":false,"given":"Xiangyu","family":"Ge","sequence":"additional","affiliation":[{"name":"College of Geography and Remote Sensing Science, Xinjiang University, Urumqi 830017, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,5,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"19023","DOI":"10.1109\/JIOT.2024.3364230","article-title":"State-of-the-Art and Future Research Challenges in UAV Swarms","volume":"11","author":"Javed","year":"2024","journal-title":"IEEE Internet Things J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Karahan, A., Demircan, N., \u00d6zgeri\u015f, M., G\u00f6k\u00e7e, O., and Karahan, F. (2025). Integration of Drones in Landscape Research: Technological Approaches and Applications. Drones, 9.","DOI":"10.3390\/drones9090603"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Platel, A., Sandino, J., Shaw, J., Bollard, B., and Gonzalez, F. (2025). Advancing Sparse Vegetation Monitoring in the Arctic and Antarctic: A Review of Satellite and UAV Remote Sensing, Machine Learning, and Sensor Fusion. Remote Sens., 17.","DOI":"10.3390\/rs17091513"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"2777","DOI":"10.1109\/TITS.2025.3584216","article-title":"A Robust Multi-Domain Adaptive Anti-Jamming Communication System for a UAV Swarm in Urban ITS Traffic Monitoring via Multi-Agent Deep Deterministic Policy Gradient","volume":"27","author":"Chen","year":"2026","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102217","DOI":"10.1016\/j.inffus.2023.102217","article-title":"A survey of multimodal hybrid deep learning for computer vision: Architectures, applications, trends, and challenges","volume":"105","author":"Bayoudh","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1904","DOI":"10.1109\/TPAMI.2015.2389824","article-title":"Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition","volume":"37","author":"He","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2505713","DOI":"10.1109\/TIM.2023.3241825","article-title":"Real-Time Object Detection Network in UAV-Vision Based on CNN and Transformer","volume":"72","author":"Ye","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_8","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. Computer Vision\u2014ECCV 2020, Springer. Proceedings, Part I.","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Wang, Q., Chi, Y., Shen, T., Song, J., Zhang, Z., and Zhu, Y. (2022). Improving RGB-Infrared Object Detection by Reducing Cross-Modality Redundancy. Remote Sens., 14.","DOI":"10.3390\/rs14092020"},{"key":"ref_11","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_12","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_13","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2018). Cascade R-CNN: Delving Into High Quality Object Detection. 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, IEEE.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Vedaldi, A., Bischof, H., Brox, T., and Frahm, J.M. (2020). Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training. Computer Vision\u2014ECCV 2020, Springer.","DOI":"10.1007\/978-3-030-58589-1"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012021","DOI":"10.1088\/1742-6596\/1948\/1\/012021","article-title":"Research Towards Yolo-Series Algorithms: Comparison and Analysis of Object Detection Models for Real-Time UAV Applications","volume":"1948","author":"Wang","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tian, Z., Shen, C., Chen, H., and He, T. (2019). FCOS: Fully Convolutional One-Stage Object Detection. 2019 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2019.00972"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7380","DOI":"10.1109\/TPAMI.2021.3119563","article-title":"Detection and Tracking Meet Drones Challenge","volume":"44","author":"Zhu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_18","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 (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Leibe, B., Matas, J., Sebe, N., and Welling, M. (2016). SSD: Single Shot MultiBox Detector. Computer Vision\u2014ECCV 2016, Springer.","DOI":"10.1007\/978-3-319-46484-8"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1109\/JSTARS.2023.3234161","article-title":"A CNN-Transformer Hybrid Model Based on CSWin Transformer for UAV Image Object Detection","volume":"16","author":"Lu","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_21","first-page":"5403021","article-title":"Cross-Modal Oriented Object Detection of UAV Aerial Images Based on Image Feature","volume":"62","author":"Wang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"111717","DOI":"10.1016\/j.patcog.2025.111717","article-title":"Real-time small object detection using adaptive weighted fusion of efficient positional features","volume":"167","author":"Ding","year":"2025","journal-title":"Pattern Recognit."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yan, X., Sun, S., Zhu, H., Hu, Q., Ying, W., and Li, Y. (2025). DMF-YOLO: Dynamic Multi-Scale Feature Fusion Network-Driven Small Target Detection in UAV Aerial Images. Remote Sens., 17.","DOI":"10.20944\/preprints202506.0837.v1"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1109\/TCSVT.2023.3286896","article-title":"High-Resolution Feature Pyramid Network for Small Object Detection on Drone View","volume":"34","author":"Chen","year":"2024","journal-title":"IEEE Trans. Circuits Syst. Video Technol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, J., Yang, W., Guo, H., Zhang, R., and Xia, G.S. (2021). Tiny Object Detection in Aerial Images. 2020 25th International Conference on Pattern Recognition (ICPR), IEEE.","DOI":"10.1109\/ICPR48806.2021.9413340"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021). TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios. 2021 IEEE\/CVF International Conference on Computer Vision Workshops (ICCVW), IEEE.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_28","unstructured":"Ferrari, V., Hebert, M., Sminchisescu, C., and Weiss, Y. (2018). SOD-MTGAN: Small Object Detection via Multi-Task Generative Adversarial Network. Computer Vision\u2014ECCV 2018, Springer."},{"key":"ref_29","first-page":"5611215","article-title":"FFCA-YOLO for Small Object Detection in Remote Sensing Images","volume":"62","author":"Zhang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"110765","DOI":"10.1016\/j.engappai.2025.110765","article-title":"Subtle Defect Detection Network: More accurately detect subtle defects on the Printed Circuit Board surface","volume":"152","author":"Zhu","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"113415","DOI":"10.1016\/j.engappai.2025.113415","article-title":"A printed circuit board surface defect detection method for long-tail and multi-scale scenarios","volume":"165","author":"Li","year":"2026","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Akyon, F.C., Onur Altinuc, S., and Temizel, A. (2022). Slicing Aided Hyper Inference and Fine-Tuning for Small Object Detection. 2022 IEEE International Conference on Image Processing (ICIP), IEEE.","DOI":"10.1109\/ICIP46576.2022.9897990"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"5643115","DOI":"10.1109\/TGRS.2024.3470900","article-title":"BRSTD: Bio-Inspired Remote Sensing Tiny Object Detection","volume":"62","author":"Huang","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_34","first-page":"5616213","article-title":"Tiny Object Detection in Remote Sensing Images Based on Object Reconstruction and Multiple Receptive Field Adaptive Feature Enhancement","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","first-page":"4704415","article-title":"A DeNoising FPN With Transformer R-CNN for Tiny Object Detection","volume":"62","author":"Liu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_36","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 (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., and Kweon, I.S. (2018, January 8\u201314). CBAM: Convolutional Block Attention Module. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021, January 20\u201325). Coordinate attention for efficient mobile network design. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Zhang, W., Huang, Q., and Tian, Q. (2018, January 8\u201314). The unmanned aerial vehicle benchmark: Object detection and tracking. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01249-6_23"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isprsjprs.2014.10.002","article-title":"Multi-class geospatial object detection and geographic image classification based on collection of part detectors","volume":"98","author":"Cheng","year":"2014","journal-title":"Isprs J. Photogramm. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Sunkara, R., and Luo, T. (2023). No more strided convolutions or pooling: A new CNN building block for low-resolution images and small objects. Machine Learning and Knowledge Discovery in Databases, Springer.","DOI":"10.1007\/978-3-031-26409-2_27"},{"key":"ref_42","first-page":"11863","article-title":"SimAM: A Simple, Parameter-Free Attention Module for Convolutional Neural Networks","volume":"Volume 139","author":"Meila","year":"2021","journal-title":"International Conference on Machine Learning; Proceedings of Machine Learning Research"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Touvron, H., Cord, M., Sablayrolles, A., Synnaeve, G., and J\u00e9gou, H. (2021). Going deeper with Image Transformers. 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV48922.2021.00010"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2017, January 18\u201323). ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Zhang, Z. (2023). Drone-YOLO: An Efficient Neural Network Method for Target Detection in Drone Images. Drones, 7.","DOI":"10.3390\/drones7080526"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Bai, Y., and Liu, Y. (2025). DL-DEIM: An Efficient and Lightweight Detection Framework with Enhanced Feature Fusion for UAV Object Detection. Appl. Sci., 15.","DOI":"10.3390\/app152211966"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Chen, H., Wang, D., Fang, J., Li, Y., Xu, S., and Xu, Z. (2024). LW-YOLOv8: An Lightweight Object Detection Algorithm for UAV Aerial Imagery. 2024 6th International Conference on Natural Language Processing (ICNLP), IEEE.","DOI":"10.1109\/ICNLP60986.2024.10692437"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chang, X., Wang, X., and Li, K. (2026). Overcoming Scale Variations and Occlusions in Aerial Detection: A Context-Aware DEIM Framework. Sensors, 26.","DOI":"10.3390\/s26010147"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Yao, B., Zhang, C., Meng, Q., Sun, X., Hu, X., Wang, L., and Li, X. (2025). SRM-YOLO for Small Object Detection in Remote Sensing Images. Remote Sens., 17.","DOI":"10.3390\/rs17122099"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2903\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T09:16:46Z","timestamp":1778145406000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/26\/9\/2903"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,6]]},"references-count":49,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["s26092903"],"URL":"https:\/\/doi.org\/10.3390\/s26092903","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,6]]}}}