{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:31:55Z","timestamp":1777735915757,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T00:00:00Z","timestamp":1746748800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62171361"],"award-info":[{"award-number":["62171361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["201805061ZD12CG45"],"award-info":[{"award-number":["201805061ZD12CG45"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Xi\u2019an Key Laboratory of Intelligent Detection and Perception","award":["62171361"],"award-info":[{"award-number":["62171361"]}]},{"name":"Xi\u2019an Key Laboratory of Intelligent Detection and Perception","award":["201805061ZD12CG45"],"award-info":[{"award-number":["201805061ZD12CG45"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>To address challenges such as large-scale variations, high density of small targets, and the large number of parameters in deep learning-based target detection models, which limit their deployment on UAV platforms with fixed performance and limited computational resources, a lightweight UAV target detection algorithm, YOLO-LSM, is proposed. First, to mitigate the loss of small target information, an Efficient Small Target Detection Layer (ESTDL) is developed, alongside structural improvements to the baseline model to reduce parameters. Second, a Multiscale Lightweight Convolution (MLConv) is designed, and a lightweight feature extraction module, MLCSP, is constructed to enhance the extraction of detailed information. Focaler inner IoU is incorporated to improve bounding box matching and localization, thereby accelerating model convergence. Finally, a novel feature fusion network, DFSPP, is proposed to enhance accuracy by optimizing the selection and adjustment of target scale ranges. Validations on the VisDrone2019 and Tiny Person datasets demonstrate that compared to the benchmark network, the YOLO-LSM achieves a mAP0.5 improvement of 6.9 and 3.5 percentage points, respectively, with a parameter count of 1.9 M, representing a reduction of approximately 72%. Different from previous work on medical detection, this study tailors YOLO-LSM for UAV-based small object detection by introducing targeted improvements in feature extraction, detection heads, and loss functions, achieving better adaptation to aerial scenarios.<\/jats:p>","DOI":"10.3390\/info16050393","type":"journal-article","created":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T04:13:44Z","timestamp":1746764024000},"page":"393","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["YOLO-LSM: A Lightweight UAV Target Detection Algorithm Based on Shallow and Multiscale Information Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-8673-450X","authenticated-orcid":false,"given":"Chenxing","family":"Wu","sequence":"first","affiliation":[{"name":"School of Defence Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-3263-6384","authenticated-orcid":false,"given":"Changlong","family":"Cai","sequence":"additional","affiliation":[{"name":"School of Defence Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Defence Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiahao","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Opto-Electronical Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yulin","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Defence Science and Technology, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Longhui","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Opto-Electronical Engineering, Xi\u2019an Technological University, Xi\u2019an 710021, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, Z., Yang, S., Qin, H., Liu, Y., and Ding, J. (2024). CCW-YOLO: A Modified YOLOv5s Network for Pedestrian Detection in Complex Traffic Scenes. Information, 15.","DOI":"10.20944\/preprints202410.2326.v1"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"\u017diguli\u0107, N., Glu\u010dina, M., Lorencin, I., and Matika, D. (2024). Military Decision-Making Process Enhanced by Image Detection. Information, 15.","DOI":"10.3390\/info15010011"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Vu, V.Q., Tran, M.-Q., Amer, M., Khatiwada, M., Ghoneim, S.S.M., and Elsisi, M. (2023). A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications. Information, 14.","DOI":"10.3390\/info14070379"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Saradopoulos, I., Potamitis, I., Rigakis, I., Konstantaras, A., and Barbounakis, I.S. (2025). Image Augmentation Using Both Background Extraction and the SAHI Approach in the Context of Vision-Based Insect Localization and Counting. Information, 16.","DOI":"10.3390\/info16010010"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Girshick, R.B., Donahue, J., Darrell, T., Malik, J., and Berkeley, U. (2014, January 23\u201328). Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA.","DOI":"10.1109\/CVPR.2014.81"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Girshick, R.B. (2015, January 7\u201313). Fast R-CNN. Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV), Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_7","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":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_8","unstructured":"Wang, A., Chen, H., Liu, L., Chen, K., Lin, Z., Han, J., and Ding, G. (2024). YOLOv10: Real-Time End-to-End Object Detection. arXiv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Yeh, I.H., and Liao, H.P. (2024). YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information. arXiv.","DOI":"10.1007\/978-3-031-72751-1_1"},{"key":"ref_10","unstructured":"Li, C., Li, L., Jiang, H., Weng, K., Geng, Y., Li, L., Ke, Z., Li, Q., Cheng, M., and Nie, W. (2022). YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S.K., Girshick, R.B., Farhadi, A., and Recognition, P. (2015, January 7\u201312). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_12","unstructured":"Khanam, R., and Hussain, M. (2024). YOLOv11: An Overview of the Key Architectural Enhancements. arXiv."},{"key":"ref_13","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 Computer Vision\u2013European Conference on Computer Vision, Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_14","unstructured":"Duan, K., Bai, S., Xie, L., Qi, H., Huang, Q., and Tian, Q. (November, January 27). CenterNet: Keypoint Triplets for Object Detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"013023","DOI":"10.1117\/1.JEI.33.1.013023","article-title":"AIOD-YOLO: An algorithm for object detection in low-altitude aerial images","volume":"33","author":"Yan","year":"2024","journal-title":"J. Electron. Imaging"},{"key":"ref_16","first-page":"1","article-title":"CFANet: Efficient Detection of UAV Image Based on Cross-Layer Feature Aggregation","volume":"61","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Min, L., Fan, Z., Lv, Q., Reda, M., Shen, L., and Wang, B. (2023). YOLO-DCTI: Small Object Detection in Remote Sensing Base on Contextual Transformer Enhancement. Remote Sens., 15.","DOI":"10.3390\/rs15163970"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"036001","DOI":"10.1088\/1402-4896\/ad2147","article-title":"A novel small object detection algorithm for UAVs based on YOLOv5","volume":"99","author":"Li","year":"2024","journal-title":"Phys. Scr."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S., and Recognition, P. (2017, January 21\u201326). Perceptual Generative Adversarial Networks for Small Object Detection. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.211"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ma, M., and Pang, H. (2023). SP-YOLOv8s: An Improved YOLOv8s Model for Remote Sensing Image Tiny Object Detection. Appl. Sci., 13.","DOI":"10.3390\/app13148161"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1109\/JSTARS.2023.3329235","article-title":"CSPPartial-YOLO: A Lightweight YOLO-Based Method for Typical Objects Detection in Remote Sensing Images","volume":"17","author":"Xie","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_22","unstructured":"Qin, D., Leichner, C., Delakis, M., Fornoni, M., Luo, S., Yang, F., Wang, W., Banbury, C.R., Ye, C., and Akin, B. (October, January 29). MobileNetV4: Universal Models for the Mobile Ecosystem. Proceedings of the European Conference on Computer Vision, Milan, Italy."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Han, K., Wang, Y., Tian, Q., Guo, J., Xu, C., Xu, C., and Recognition, P. (2020, January 13\u201319). GhostNet: More Features From Cheap Operations. Proceedings of the 2020 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00165"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Cai, Z., and Shen, Q.J.A. (2023). FalconNet: Factorization for the Light-weight ConvNets. arXiv.","DOI":"10.1007\/978-981-99-8079-6_29"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hu, M., Li, Z., Yu, J., Wan, X., Tan, H., and Lin, Z. (2023). Efficient-Lightweight YOLO: Improving Small Object Detection in YOLO for Aerial Images. Sensors, 23.","DOI":"10.3390\/s23146423"},{"key":"ref_26","unstructured":"Yu, Z., Guan, Q., Yang, J., Yang, Z., Zhou, Q., Chen, Y., and Chen, F. (2024). LSM-YOLO: A Compact and Effective ROI Detector for Medical Detection. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Zhu, P., Du, D., Wen, L., Bian, X., Ling, H., Hu, Q., Peng, T., Zheng, J., Wang, X., and Zhang, Y. (2019, January 27\u201328). VisDrone-DET2019: The Vision Meets Drone Object Detection in Image Challenge Results. Proceedings of the IEEE\/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Republic of Korea.","DOI":"10.1109\/ICCVW.2019.00031"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yu, X., Gong, Y., Jiang, N., Ye, Q., and Han, Z. (2020, January 1\u20135). Scale Match for Tiny Person Detection. Proceedings of the 2020 IEEE Winter Conference on Applications of Computer Vision, Snowmass, CO, USA.","DOI":"10.1109\/WACV45572.2020.9093394"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","article-title":"The Pascal Visual Object Classes (VOC) Challenge","volume":"88","author":"Everingham","year":"2010","journal-title":"Int. J. Comput. Vis."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhang, R., Shao, Z., Huang, X., Wang, J., and Li, D. (2020). Object Detection in UAV Images via Global Density Fused Convolutional Network. Remote. Sens., 12.","DOI":"10.3390\/rs12193140"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"134846","DOI":"10.1109\/ACCESS.2023.3334973","article-title":"The Accelerated Inference of a Novel Optimized YOLOv5-LITE on Low-Power Devices for Railway Track Damage Detection","volume":"11","author":"Dang","year":"2023","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., Liao, H.-Y.M., and Recognition, P. (2023, January 17\u201324). YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors. Proceedings of the 2023 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"318","DOI":"10.1109\/TPAMI.2018.2858826","article-title":"Focal Loss for Dense Object Detection","volume":"42","author":"Lin","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"086005","DOI":"10.1088\/1402-4896\/ad6496","article-title":"URS-YOLOv5s: Object detection algorithm for UAV remote sensing images","volume":"99","author":"Bi","year":"2024","journal-title":"Phys. Scr."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"124848","DOI":"10.1016\/j.eswa.2024.124848","article-title":"EL-YOLO: An efficient and lightweight low-altitude aerial objects detector for onboard applications","volume":"256","author":"Xue","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Gao, S., Gao, M., and Wei, Z. (2024). MCF-YOLOv5: A Small Target Detection Algorithm Based on Multi-Scale Feature Fusion Improved YOLOv5. Information, 15.","DOI":"10.3390\/info15050285"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"066003","DOI":"10.1088\/1402-4896\/ad418f","article-title":"BD-YOLO: Detection algorithm for high-resolution remote sensing images","volume":"99","author":"Lou","year":"2024","journal-title":"Phys. Scr."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"013003","DOI":"10.1117\/1.JEI.34.1.013003","article-title":"LIS-DETR: Small target detection transformer for autonomous driving based on learned inverted residual cascaded group","volume":"34","author":"Chen","year":"2025","journal-title":"J. Electron. Imaging"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"015204","DOI":"10.1088\/2631-8695\/ada489","article-title":"FNI-DETR: Real-time DETR with far and near feature interaction for small object detection","volume":"7","author":"Han","year":"2025","journal-title":"Eng. Res. Express"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"100523","DOI":"10.1016\/j.eij.2024.100523","article-title":"YOLO-HyperVision: A vision transformer backbone-based enhancement of YOLOv5 for detection of dynamic traffic information","volume":"27","author":"Xu","year":"2024","journal-title":"Egypt. Inform. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, J., Kao, S.-h., He, H., Zhuo, W., Wen, S., Lee, C.-H., and Chan, S.-H.G. (2023, January 17\u201324). Run, Don\u2019t Walk: Chasing Higher FLOPS for Faster Neural Networks. Proceedings of the IEEE\/CVF Conference on Computer Vision Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.01157"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Li, J., Wen, Y., and He, L. (2023, January 17\u201324). SCConv: Spatial and Channel Reconstruction Convolution for Feature Redundancy. Proceedings of the IEEE\/CVF Conference on Computer Vision Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00596"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/393\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:29:53Z","timestamp":1760030993000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/16\/5\/393"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,9]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2025,5]]}},"alternative-id":["info16050393"],"URL":"https:\/\/doi.org\/10.3390\/info16050393","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,9]]}}}