{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,6]],"date-time":"2026-11-06T12:18:49Z","timestamp":1793967529939,"version":"4.8.0"},"reference-count":39,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T00:00:00Z","timestamp":1763596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Fundamental Research Funds for the Central Universities, CHD","award":["GLDX2024K05"],"award-info":[{"award-number":["GLDX2024K05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unmanned aerial vehicle (UAV) vision systems have been widely deployed for aerial monitoring applications, yet small-target detection in UAV imagery remains a significant challenge due to minimal pixel representation, substantial scale variations, complex background interference, and varying illumination conditions. Existing object detection algorithms struggle to maintain high accuracy when processing small targets with fewer than 32 \u00d7 32 pixels in UAV-captured scenes, particularly in complex environments where target-background confusion is prevalent. To address these limitations, this study proposes RMH-YOLO, a refined multi-scale architecture. The model incorporates four key innovations: a Refined Feature Module (RFM) that fuses channel and spatial attention mechanisms to enhance weak feature representation of small targets while maintaining contextual integrity; a Multi-scale Focus-and-Diffuse (MFFD) network that employs a focus-diffuse transmission pathway to preserve fine-grained spatial details from high-resolution layers and propagate them to semantic features; an efficient CS-Head detection architecture that utilizes parameter-sharing convolution to enable efficient processing on embedded platforms; and an optimized loss function combining Normalized Wasserstein Distance (NWD) with InnerCIoU to improve localization accuracy for small targets. Experimental validation on the VisDrone2019 dataset demonstrates that RMH-YOLO achieves a precision and recall of 53.0% and 40.4%, representing improvements of 8.8% and 7.4% over the YOLOv8n baseline. The proposed method attains mAP50 and mAP50:95 of 42.4% and 25.7%, corresponding to enhancements of 9.2% and 6.4%, respectively, while maintaining computational efficiency with only 1.3 M parameters and 16.7 G FLOPs. Experimental results confirm that RMH-YOLO effectively improves small-target detection accuracy while maintaining computational efficiency, demonstrating its broad application potential in diverse UAV aerial monitoring scenarios.<\/jats:p>","DOI":"10.3390\/s25227088","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:25:27Z","timestamp":1763645127000},"page":"7088","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["RMH-YOLO: A Refined Multi-Scale Architecture for Small-Target Detection in UAV Aerial Imagery"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0409-6666","authenticated-orcid":false,"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"Highway School, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min","family":"He","sequence":"additional","affiliation":[{"name":"School of Architecture and Civil Engineering, Xi\u2019an University of Science and Technology, Xi\u2019an 710064, China"},{"name":"Research Center of Highway Large Structure Engineering on Safety, Ministry of Education, Chang\u2019an University, Xi\u2019 an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiuxian","family":"Liu","sequence":"additional","affiliation":[{"name":"Highway School, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0355-5660","authenticated-orcid":false,"given":"Haochen","family":"Jin","sequence":"additional","affiliation":[{"name":"Highway School, Chang\u2019an University, Xi\u2019an 710064, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ogundele, O.M., Tamrakar, N., Kook, J.-H., Kim, S.-M., Choi, J.-I., Karki, S., Akpenpuun, T.D., and Kim, H.T. (2025). Real-Time Strawberry Ripeness Classification and Counting: An Optimized YOLOv8s Framework with Class-Aware Multi-Object Tracking. Agriculture, 15.","DOI":"10.3390\/agriculture15181906"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Xu, K., Hou, Y., Sun, W., Chen, D., Lv, D., Xing, J., and Yang, R. (2025). A Detection Method for Sweet Potato Leaf Spot Disease and Leaf-Eating Pests. Agriculture, 15.","DOI":"10.3390\/agriculture15050503"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Li, W., Huang, L., and Lai, X. (2025). A Deep Learning Framework for Traffic Accident Detection Based on Improved YOLO11. Vehicles, 7.","DOI":"10.3390\/vehicles7030081"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kim, S.-K., and Chan, I.C. (2025). Novel Machine Learning-Based Smart City Pedestrian Road Crossing Alerts. Smart Cities, 8.","DOI":"10.3390\/smartcities8040114"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gao, B., Jia, W., Wang, Q., and Yang, G. (2025). All-Weather Forest Fire Automatic Monitoring and Early Warning Application Based on Multi-Source Remote Sensing Data: Case Study of Yunnan. Fire, 8.","DOI":"10.3390\/fire8090344"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Meng, X., and Wang, J. (2025). SFGI-YOLO: A Multi-Scale Detection Method for Early Forest Fire Smoke Using an Extended Receptive Field. Forests, 16.","DOI":"10.3390\/f16081345"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Tao, S., and Zheng, J. (2025). Leveraging Prototypical Prompt Learning for Robust Bridge Defect Classification in Civil Infrastructure. Electronics, 14.","DOI":"10.3390\/electronics14071407"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Su, Y., Song, Y., Zhan, Z., Bi, Z., Zhou, B., Yu, Y., and Song, Y. (2025). Research on Intelligent Identification Technology for Bridge Cracks. Infrastructures, 10.","DOI":"10.3390\/infrastructures10050102"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lei, P., Wang, C., and Liu, P. (2025). RPS-YOLO: A Recursive Pyramid Structure-Based YOLO Network for Small Object Detection in Unmanned Aerial Vehicle Scenarios. Appl. Sci., 15.","DOI":"10.3390\/app15042039"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Wang, H., and Gao, J. (2025). SF-DETR: A Scale-Frequency Detection Transformer for Drone-View Object Detection. Sensors, 25.","DOI":"10.3390\/s25072190"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Wei, Y. (2025). UAV-DETR: An Enhanced RT-DETR Architecture for Efficient Small Object Detection in UAV Imagery. Sensors, 25.","DOI":"10.3390\/s25154582"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wan, Z., Lan, Y., Xu, Z., Shang, K., and Zhang, F. (2025). DAU-YOLO: A Lightweight and Effective Method for Small Object Detection in UAV Images. Remote Sens., 17.","DOI":"10.3390\/rs17101768"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 7\u201313). Fast r-cnn. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_15","unstructured":"Wei, L., Dragomir, A., Dumitru, E., Christian, S., Scott, R., Cheng-Yang, F., and Berg, A.C. (2016). SSD: Single Shot MultiBox Detector, Springer."},{"key":"ref_16","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_17","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 Computer Vision & Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, Faster, Stronger. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhao, S., Chen, H., Zhang, D., Tao, Y., Feng, X., and Zhang, D. (2025). SR-YOLO: Spatial-to-Depth Enhanced Multi-Scale Attention Network for Small Target Detection in UAV Aerial Imagery. Remote Sens., 17.","DOI":"10.3390\/rs17142441"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"104964","DOI":"10.1016\/j.dsp.2024.104964","article-title":"Enhanced YOLOv8 for small-object detection in multiscale UAV imagery: Innovations in detection accuracy and efficiency","volume":"158","author":"Luo","year":"2025","journal-title":"Digit. Signal Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"113936","DOI":"10.1016\/j.measurement.2023.113936","article-title":"STF-YOLO: A small target detection algorithm for UAV remote sensing images based on improved SwinTransformer and class weighted classification decoupling head","volume":"224","author":"Hui","year":"2024","journal-title":"Measurement"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"126384","DOI":"10.1016\/j.neucom.2023.126384","article-title":"PETNet: A YOLO-based prior enhanced transformer network for aerial image detection","volume":"547","author":"Wang","year":"2023","journal-title":"Neurocomputing"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Bi, J., Li, K., Zheng, X., Zhang, G., and Lei, T. (2025). SPDC-YOLO: An Efficient Small Target Detection Network Based on Improved YOLOv8 for Drone Aerial Image. Remote Sens., 17.","DOI":"10.3390\/rs17040685"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Liao, D., Bi, R., Zheng, Y., Hua, C., Huang, L., Tian, X., and Liao, B. (2025). LCW-YOLO: An Explainable Computer Vision Model for Small Object Detection in Drone Images. Appl. Sci., 15.","DOI":"10.3390\/app15179730"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Li, Y., Li, Q., Pan, J., Zhou, Y., Zhu, H., Wei, H., and Liu, C. (2024). SOD-YOLO: Small-Object-Detection Algorithm Based on Improved YOLOv8 for UAV Images. Remote Sens., 16.","DOI":"10.3390\/rs16163057"},{"key":"ref_26","unstructured":"Xu, X., Jiang, Y., Chen, W., Huang, Y.L., Zhang, Y., and Sun, X. (2022). DAMO-YOLO: A Report on Real-Time Object Detection Design. arxiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, Y., Zhang, C., Chen, B., Huang, Y., Sun, Y., Wang, C., Fu, X., Dai, Y., Qin, F., and Peng, Y. (2024). Accurate Leukocyte Detection Based on Deformable-DETR and Multi-Level Feature Fusion for Aiding Diagnosis of Blood Diseases. Comput. Biol. Med., 170.","DOI":"10.1016\/j.compbiomed.2024.107917"},{"key":"ref_28","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 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., and Yang, J. (2020). Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection. arXiv.","DOI":"10.1109\/CVPR46437.2021.01146"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, S., Chi, C., Yao, Y., Lei, Z., and Li, S.Z. (2019, January 5). Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00978"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Vasconcelos, N. (2017, January 21\u201326). Cascade R-CNN: Delving Into High Quality Object Detection. Proceedings of the 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Honolulu, Hawaii, USA.","DOI":"10.1109\/CVPR.2018.00644"},{"key":"ref_32","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_33","doi-asserted-by":"crossref","unstructured":"Feng, C., Zhong, Y., Gao, Y., Scott, M.R., and Huang, W. (2021, January 10\u201317). TOOD: Task-aligned One-stage Object Detection. Proceedings of the 2021 IEEE\/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada.","DOI":"10.1109\/ICCV48922.2021.00349"},{"key":"ref_34","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 2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","unstructured":"Yue, M., Zhang, L., Huang, J., and Zhang, H. (2024). Lightweight and Efficient Tiny-Object Detection Based on Improved YOLOv8n for UAV Aerial Images. Drones, 8.","DOI":"10.3390\/drones8070276"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Luo, J., Liu, Z., Wang, Y., Tang, A., Zuo, H., and Han, P. (2024). Efficient Small Object Detection You Only Look Once: A Small Object Detection Algorithm for Aerial Images. Sensors, 24.","DOI":"10.3390\/s24217067"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"5459","DOI":"10.1007\/s40747-024-01448-6","article-title":"TA-YOLO: A lightweight small object detection model based on multi-dimensional trans-attention module for remote sensing images","volume":"10","author":"Li","year":"2024","journal-title":"Complex Intell. Syst."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"126440","DOI":"10.1016\/j.eswa.2025.126440","article-title":"Precision and speed: LSOD-YOLO for lightweight small object detection","volume":"269","author":"Wang","year":"2025","journal-title":"Expert Syst. Appl."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/7088\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T13:28:00Z","timestamp":1763645280000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/22\/7088"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,20]]},"references-count":39,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2025,11]]}},"alternative-id":["s25227088"],"URL":"https:\/\/doi.org\/10.3390\/s25227088","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,20]]}}}