{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:27:38Z","timestamp":1784737658837,"version":"3.55.0"},"reference-count":32,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,12,26]],"date-time":"2023-12-26T00:00:00Z","timestamp":1703548800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Double Thousand Plan Program of Jiangxi Province","award":["jxsq2023101100"],"award-info":[{"award-number":["jxsq2023101100"]}]},{"name":"Double Thousand Plan Program of Jiangxi Province","award":["20212ABC03A03"],"award-info":[{"award-number":["20212ABC03A03"]}]},{"name":"03 and 5G project of Jiangxi Province","award":["jxsq2023101100"],"award-info":[{"award-number":["jxsq2023101100"]}]},{"name":"03 and 5G project of Jiangxi Province","award":["20212ABC03A03"],"award-info":[{"award-number":["20212ABC03A03"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Object detection in drone aerial imagery has been a consistent focal point of research. Aerial images present more intricate backgrounds, greater variation in object scale, and a higher occurrence of small objects compared to standard images. Consequently, conventional object detection algorithms are often unsuitable for direct application in drone scenarios. To address these challenges, this study proposes a drone object detection algorithm model based on YOLOv5, named SMT-YOLOv5 (Small Target-YOLOv5). The enhancement strategy involves improving the feature fusion network by incorporating detection layers and implementing a weighted bidirectional feature pyramid network. Additionally, the introduction of the Combine Attention and Receptive Fields Block (CARFB) receptive field feature extraction module and DyHead dynamic target detection head aims to broaden the receptive field, mitigate information loss, and enhance perceptual capabilities in spatial, scale, and task domains. Experimental validation on the VisDrone2021 dataset confirms a significant improvement in the target detection accuracy of SMT-YOLOv5. Each improvement strategy yields effective results, raising the average precision by 12.4 percentage points compared to the original method. Detection improvements for large, medium, and small targets increase by 6.9%, 9.5%, and 7.7%, respectively, compared to the original method. Similarly, applying the same improvement strategies to the low-complexity YOLOv8n results in SMT-YOLOv8n, which is comparable in complexity to SMT-YOLOv5s. The results indicate that, relative to SMT-YOLOv8n, SMT-YOLOv5s achieves a 2.5 percentage point increase in average precision. Furthermore, comparative experiments with other enhancement methods demonstrate the effectiveness of the improvement strategies.<\/jats:p>","DOI":"10.3390\/s24010134","type":"journal-article","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T02:58:12Z","timestamp":1703645892000},"page":"134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":40,"title":["Small Target-YOLOv5: Enhancing the Algorithm for Small Object Detection in Drone Aerial Imagery Based on YOLOv5"],"prefix":"10.3390","volume":"24","author":[{"given":"Jiachen","family":"Zhou","sequence":"first","affiliation":[{"name":"School of General Aviation, Nanchang Hangkong University, Nanchang 330063, China"},{"name":"School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taoyong","family":"Su","sequence":"additional","affiliation":[{"name":"School of General Aviation, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kewei","family":"Li","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyang","family":"Dai","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Nanchang Hangkong University, Nanchang 330063, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,12,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9775","DOI":"10.4249\/scholarpedia.9775","article-title":"Local binary patterns","volume":"5","year":"2010","journal-title":"Scholarpedia"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10491","DOI":"10.4249\/scholarpedia.10491","article-title":"Scale invariant feature transform","volume":"7","author":"Lindeberg","year":"2012","journal-title":"Scholarpedia"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1292","DOI":"10.1109\/TSMCB.2003.818533","article-title":"Gray-scale image enhancement as an automatic process driven by evolution","volume":"34","author":"Munteanu","year":"2004","journal-title":"IEEE Trans. Syst. Man Cybern. Part B (Cybern.)"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1007\/BF00116037","article-title":"The strength of weak learnability","volume":"5","author":"Schapire","year":"1990","journal-title":"Mach. Learn."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"123","DOI":"10.1007\/BF00058655","article-title":"Bagging predictors","volume":"24","author":"Breiman","year":"1996","journal-title":"Mach. Learn."},{"key":"ref_6","unstructured":"Yu, S.P. (2018). Design and Implementation of Vision Based Drone Intrusion Detection and Tracking System. [Master\u2019s Thesis, Zhejiang University]. Available online: https:\/\/kns.cnki.net\/kcms2\/article\/abstract?v=1u4N9e-cd2SsuRW_0BNTub8JN-A6xqxElkQU5Xb6nL4cOf6al0PzM23FjILwB6b81iVzc64LGEYM5ir_rw-PfXiNKb-U0k7fhyITEETIpe40qIZIzEIObhEx_lUtbc5S51mszVTwuxY=&uniplatform=NZKPT&language=CHS."},{"key":"ref_7","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_8","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_9","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, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_10","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\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands. Proceedings, Part I 14.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C.L. (2014, January 6\u201312). Microsoft coco: Common objects in context. Proceedings of the Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland. Proceedings, Part V 13.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Liu, M., Wang, X., Zhou, A., Fu, X., Ma, Y., and Piao, C. (2020). UAV-YOLO: Small Object Detection on Unmanned Aerial Vehicle Perspective. Sensors, 20.","DOI":"10.3390\/s20082238"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Luo, X., Wu, Y., and Wang, F. (2022). Target detection method of UAV aerial imagery based on improved YOLOv5. Remote Sens., 14.","DOI":"10.3390\/rs14195063"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Zhou, H., Ma, A., Niu, Y., and Ma, Z. (2022). Small-object detection for UAV-based images using a distance metric method. Drones, 6.","DOI":"10.3390\/drones6100308"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"104697","DOI":"10.1016\/j.imavis.2023.104697","article-title":"Improved YOLOX-X based UAV aerial photography object detection algorithm","volume":"135","author":"Wang","year":"2023","journal-title":"Image Vis. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"145740","DOI":"10.1109\/ACCESS.2020.3014910","article-title":"Small-object detection in UAV-captured images via multi-branch parallel feature pyramid networks","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wu, Q., Zhang, B., Guo, C., and Wang, L. (2023). Multi-Branch Parallel Networks for Object Detection in High-Resolution UAV Remote Sensing Images. Drones, 7.","DOI":"10.3390\/drones7070439"},{"key":"ref_18","unstructured":"Jocher, G., Stoken, A., Borovec, J., Changyu, L., Hogan, A., Diaconu, L., and Re\u00f1\u00e9 Claramunt, E. (2022, November 22). ultralytics\/yolov5: v3. 0.Zenodo. Available online: https:\/\/ui.adsabs.harvard.edu\/link_gateway\/2020zndo...3983579J\/doi:10.5281\/zenodo.3983579."},{"key":"ref_19","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 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01079"},{"key":"ref_20","first-page":"59","article-title":"Feature Fusion Method for Object Detection","volume":"4","author":"Zheng","year":"2022","journal-title":"J. Nanchang Hangkong Univ. (Nat. Sci. Ed.)"},{"key":"ref_21","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_22","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, SALT Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00913"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Liu, S., and Huang, D. (2018, January 8\u201314). Receptive field block net for accurate and fast object detection. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-01252-6_24"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","unstructured":"Dai, X., Chen, Y., Xiao, B., Chen, D., Liu, M., Yuan, L., and Zhang, L. (2021, January 20\u201325). Dynamic head: Unifying object detection heads with attentions. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.00729"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Cao, Y., He, Z., Wang, L., Wang, W., Yuan, Y., Zhang, D., Zhang, J., Zhu, P., Van Gool, L., and Han, J. (2021, January 10\u201317). VisDrone-DET2021: The vision meets drone object detection challenge results. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, QC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00319"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_28","unstructured":"Redmon, J., and Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Wang, C.Y., Bochkovskiy, A., and Liao, H.Y.M. (2023, January 18\u201322). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, BC, Canada.","DOI":"10.1109\/CVPR52729.2023.00721"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yang, R., Li, W., Shang, X., Zhu, D., and Man, X. (2023). KPE-YOLOv5: An Improved Small Target Detection Algorithm Based on YOLOv5. Electronics, 12.","DOI":"10.3390\/electronics12040817"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Guo, J., Liu, X., Bi, L., Liu, H., and Lou, H. (2023). Un-yolov5s: A uav-based aerial photography detection algorithm. Sensors, 23.","DOI":"10.3390\/s23135907"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"103752","DOI":"10.1016\/j.jvcir.2023.103752","article-title":"FE-YOLOv5: Feature enhancement network based on YOLOv5 for small object detection","volume":"90","author":"Wang","year":"2023","journal-title":"J. Vis. Commun. Image Represent."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/134\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:42:13Z","timestamp":1760132533000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/1\/134"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,26]]},"references-count":32,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2024,1]]}},"alternative-id":["s24010134"],"URL":"https:\/\/doi.org\/10.3390\/s24010134","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,26]]}}}