{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:43:07Z","timestamp":1777092187310,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T00:00:00Z","timestamp":1776038400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Despite over two decades of advancement in object detection, achieving high accuracy for small target detection in practical applications remains an unresolved challenge. This paper proposes a novel small-object detection model to address this issue. The model incorporates three key innovations: first, the RCSOSA module, which optimizes feature information transmission through dynamic channel interaction and multi-scale feature coordination; second, the HFPN module, a three-branch multi-scale feature fusion network that integrates local and global features by combining CNN and Transformer architectures to enhance semantic details; and third, the NWD-CIoU loss function, which dynamically adjusts the weights of NWD and CIoU losses based on the training phase. Experimental results on the COCO dataset demonstrate that our model improves detection accuracy by 4% over YOLOv11 and achieves state-of-the-art performance among mainstream models while maintaining a real-time inference speed of no less than 60 FPS. Furthermore, validation on the VisDrone dataset confirms the model\u2019s strong generalization capability. The proposed algorithm significantly enhances small target detection accuracy, effectively mitigating a critical limitation in current practical object detection applications.<\/jats:p>","DOI":"10.3390\/a19040306","type":"journal-article","created":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T13:12:26Z","timestamp":1776085946000},"page":"306","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["RCS-HFPN-YOLOV11: A New Small Target Detection Model"],"prefix":"10.3390","volume":"19","author":[{"given":"Hong","family":"Zhang","sequence":"first","affiliation":[{"name":"Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Runzhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhengqing","family":"Zhu","sequence":"additional","affiliation":[{"name":"Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Feng","sequence":"additional","affiliation":[{"name":"Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yin, Y., and Shao, Z. (2023). An Enhanced Target Detection Algorithm for Maritime Search and Rescue Based on Aerial Images. Remote Sens., 15.","DOI":"10.3390\/rs15194818"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"33384","DOI":"10.1109\/ACCESS.2024.3355199","article-title":"Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles","volume":"12","author":"Zhao","year":"2024","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2245","DOI":"10.1109\/JIOT.2021.3128440","article-title":"Monocular 3-D Object Detection Based on Depth-Guided Local Convolution for Smart Payment in D2D Systems","volume":"10","author":"Li","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Iwanowski, M., and Gahbler, M. (2025). Multiple Large AI Models\u2019 Consensus for Object Detection\u2014A Survey. Appl. Sci., 15.","DOI":"10.20944\/preprints202511.0879.v1"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"103575","DOI":"10.1016\/j.inffus.2025.103575","article-title":"Object detection with multimodal large vision-language models: An in-depth review","volume":"126","author":"Sapkota","year":"2026","journal-title":"Inf. Fusion"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"109256","DOI":"10.1016\/j.sigpro.2023.109256","article-title":"Fusion detection in distributed MIMO radar under hybrid-order Gaussian model","volume":"214","author":"Jing","year":"2024","journal-title":"Signal Process."},{"key":"ref_7","first-page":"4102111","article-title":"Automatic SAR Ship Detection Based on Multifeature Fusion Network in Spatial and Frequency Domains","volume":"61","author":"Wang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2201382","DOI":"10.1002\/adom.202201382","article-title":"A Single Metasurface Can Perform Range-Velocity Detection and Target Imaging Simultaneously at Single Frequency","volume":"10","author":"Huang","year":"2022","journal-title":"Adv. Opt. Mater."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"065005","DOI":"10.1088\/1361-6501\/acbd21","article-title":"Vehicle door frame positioning method for binocular vision robots based on improved YOLOv4","volume":"34","author":"Song","year":"2023","journal-title":"Meas. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, Q., Chen, X., Wang, B., Liu, J., Zhang, G., and Feng, B. (2023). Shot Boundary Detection Based on Global Features and the Target Features. Symmetry, 15.","DOI":"10.3390\/sym15030565"},{"key":"ref_11","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 (CVPR) 2014, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), IEEE.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_13","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_14","doi-asserted-by":"crossref","first-page":"7507","DOI":"10.1109\/JSTARS.2023.3303692","article-title":"Optical Remote Sensing Image Target Detection Based on Improved Feature Pyramid","volume":"16","author":"Wei","year":"2023","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"10781","DOI":"10.1007\/s11042-022-13305-0","article-title":"Detection and tracking of safety helmet based on DeepSort and YOLOv5","volume":"82","author":"Song","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Varghese, R., and M., S. (2024, January 18\u201319). YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness. Proceedings of the 2024 International Conference on Advances in Data Engineering and Intelligent Computing Systems (ADICS), Chennai, India.","DOI":"10.1109\/ADICS58448.2024.10533619"},{"key":"ref_17","first-page":"355","article-title":"An improved SSD lightweight network with coordinate attention for aircraft target recognition in scene videos","volume":"46","author":"Li","year":"2024","journal-title":"J. Intell. Fuzzy Syst."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Teju, V., Sowmya, K.V., Kandula, S.R., Stan, A., and Stan, O.P. (2023). A Hybrid Retina Net Classifier for Thermal Imaging. Appl. Sci., 13.","DOI":"10.3390\/app13148525"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Wang, X., Shi, H., Wang, K., Tian, Y., Xu, Z., Zhang, Y., and Jia, G. (2025). BRA-YOLOv10: UAV Small Target Detection Based on YOLOv10. Drones, 9.","DOI":"10.3390\/drones9030159"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"105469","DOI":"10.1016\/j.imavis.2025.105469","article-title":"SFFEF-YOLO: Small object detection network based on fine-grained feature extraction and fusion for unmanned aerial images","volume":"156","author":"Bai","year":"2025","journal-title":"Image Vis. Comput."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"045402","DOI":"10.1088\/1361-6501\/adbccb","article-title":"MASNet: A novel deep learning approach for enhanced detection of small targets in complex scenarios","volume":"36","author":"Zhang","year":"2025","journal-title":"Meas. Sci. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1007\/s11760-025-03850-0","article-title":"Small object detection in UAV imagery based on channel-spatial fusion cross attention","volume":"19","author":"Li","year":"2025","journal-title":"Signal Image Video Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"116534","DOI":"10.1109\/ACCESS.2023.3325677","article-title":"UAV Target Detection Algorithm Based on Improved YOLOv8","volume":"11","author":"Wang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Ju, Z., Sun, T., Dong, F., Li, J., Yang, R., Fu, Q., Lian, C., and Shan, P. (2023). TGC-YOLOv5: An Enhanced YOLOv5 Drone Detection Model Based on Transformer, GAM & CA Attention Mechanism. Drones, 7.","DOI":"10.3390\/drones7070446"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1049\/ipr2.13009","article-title":"DGW-YOLOv8: A small insulator target detection algorithm based on deformable attention backbone and WIoU loss function","volume":"18","author":"Hu","year":"2023","journal-title":"IET Image Process."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"111066","DOI":"10.1016\/j.knosys.2023.111066","article-title":"GRA-Net: Global receptive attention network for surface defect detection","volume":"280","author":"Xiao","year":"2023","journal-title":"Knowl.-Based Syst."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qu, Y., Wang, C., Xiao, Y., Yu, J., Chen, X., and Kong, Y. (2023). Optimization Algorithm for Surface Defect Detection of Aircraft Engine Components Based on YOLOv5. Appl. Sci., 13.","DOI":"10.3390\/app132011344"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"106359","DOI":"10.1016\/j.engappai.2023.106359","article-title":"PCB defects target detection combining multi-scale and attention mechanism","volume":"123","author":"Jiang","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2425","DOI":"10.1177\/09544062231187606","article-title":"TBi-YOLOv5: A surface defect detection model for crane wire with Bottleneck Transformer and small target detection layer","volume":"238","author":"Huang","year":"2023","journal-title":"Proc. Inst. Mech. Eng. Part C J. Mech. Eng. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"7558","DOI":"10.1038\/s41598-025-88184-0","article-title":"YOLO-BS: A traffic sign detection algorithm based on YOLOv8","volume":"15","author":"Zhang","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2023.11.008","article-title":"An improved YOLO algorithm for detecting flowers and fruits on strawberry seedlings","volume":"237","author":"Bai","year":"2024","journal-title":"Biosyst. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Lv, M., and Su, W.-H. (2024). YOLOV5-CBAM-C3TR: An optimized model based on transformer module and attention mechanism for apple leaf disease detection. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1323301"},{"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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2024, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.01605"},{"key":"ref_35","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_36","unstructured":"Ramachandran, P., Zoph, B., and Le, Q.V. (2017). Swish: A Self-Gated Activation Function. arXiv."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arXiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Kang, M., Ting, C.-M., Ting, F.F., and Phan, R.C.W. (2023). RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection. Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2023, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-43901-8_57"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"105534","DOI":"10.1016\/j.bspc.2023.105534","article-title":"HiFuse: Hierarchical multi-scale feature fusion network for medical image classification","volume":"87","author":"Huo","year":"2024","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_40","unstructured":"Wang, J., Xu, C., Yang, W., and Yu, L. (2021). A Normalized Gaussian Wasserstein Distance for Tiny Object Detection. arXiv."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/306\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T04:11:24Z","timestamp":1777090284000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/19\/4\/306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,13]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["a19040306"],"URL":"https:\/\/doi.org\/10.3390\/a19040306","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,13]]}}}