{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:39:20Z","timestamp":1793129960051,"version":"4.5.0"},"reference-count":28,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T00:00:00Z","timestamp":1750204800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The National Key R&amp;D Program of China","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"The National Key R&amp;D Program of China","award":["XDA26010200"],"award-info":[{"award-number":["XDA26010200"]}]},{"name":"The National Key R&amp;D Program of China","award":["24YFYSHZ00080"],"award-info":[{"award-number":["24YFYSHZ00080"]}]},{"name":"The National Key R&amp;D Program of China","award":["DLLJ202206"],"award-info":[{"award-number":["DLLJ202206"]}]},{"name":"The National Key R&amp;D Program of China","award":["YC2023-S560"],"award-info":[{"award-number":["YC2023-S560"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["XDA26010200"],"award-info":[{"award-number":["XDA26010200"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["24YFYSHZ00080"],"award-info":[{"award-number":["24YFYSHZ00080"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["DLLJ202206"],"award-info":[{"award-number":["DLLJ202206"]}]},{"name":"Strategic Priority Research Program of the Chinese Academy of Sciences","award":["YC2023-S560"],"award-info":[{"award-number":["YC2023-S560"]}]},{"name":"Science and Technology Program of Tianjin","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"Science and Technology Program of Tianjin","award":["XDA26010200"],"award-info":[{"award-number":["XDA26010200"]}]},{"name":"Science and Technology Program of Tianjin","award":["24YFYSHZ00080"],"award-info":[{"award-number":["24YFYSHZ00080"]}]},{"name":"Science and Technology Program of Tianjin","award":["DLLJ202206"],"award-info":[{"award-number":["DLLJ202206"]}]},{"name":"Science and Technology Program of Tianjin","award":["YC2023-S560"],"award-info":[{"award-number":["YC2023-S560"]}]},{"name":"The Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"The Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["XDA26010200"],"award-info":[{"award-number":["XDA26010200"]}]},{"name":"The Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["24YFYSHZ00080"],"award-info":[{"award-number":["24YFYSHZ00080"]}]},{"name":"The Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["DLLJ202206"],"award-info":[{"award-number":["DLLJ202206"]}]},{"name":"The Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology","award":["YC2023-S560"],"award-info":[{"award-number":["YC2023-S560"]}]},{"name":"Graduate Innovative Special Fund Projects of Jiangxi Province","award":["2021YFD1300501"],"award-info":[{"award-number":["2021YFD1300501"]}]},{"name":"Graduate Innovative Special Fund Projects of Jiangxi Province","award":["XDA26010200"],"award-info":[{"award-number":["XDA26010200"]}]},{"name":"Graduate Innovative Special Fund Projects of Jiangxi Province","award":["24YFYSHZ00080"],"award-info":[{"award-number":["24YFYSHZ00080"]}]},{"name":"Graduate Innovative Special Fund Projects of Jiangxi Province","award":["DLLJ202206"],"award-info":[{"award-number":["DLLJ202206"]}]},{"name":"Graduate Innovative Special Fund Projects of Jiangxi Province","award":["YC2023-S560"],"award-info":[{"award-number":["YC2023-S560"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Animals"],"abstract":"<jats:p>Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage\u2013livestock balance assessment. These tasks are integral to the modernization and upgrading of the livestock industry and the sustainable development of grasslands. Unmanned aerial vehicles (UAVs) provide significant advantages in flexibility and maneuverability, making them ideal for livestock population surveys. However, grazing livestock in UAV images often appear small and densely packed, leading to identification errors. To address this challenge, we propose an efficient Livestock Network (LSNET) algorithm, a novel YOLOv7-based network. Our approach incorporates a low-level prediction head (P2) to detect small objects from shallow feature maps, while removing a deep-level prediction head (P5) to mitigate the effects of excessive down-sampling. To capture high-level semantic features, we introduce the Large Kernel Attentions Spatial Pyramid Pooling (LKASPP) module. In addition, we replaced the original CIoU with the WIoU v3 loss function. Furthermore, we developed a dataset of grazing livestock for deep learning using UAV images from the Prairie Chenbarhu Banner in Hulunbuir, Inner Mongolia. Our results demonstrate that the proposed module significantly improves the detection accuracy for small livestock objects, with the mean Average Precision (mAP) increasing by 1.47% compared to YOLOv7. Thus, this work offers a novel and practical solution for livestock detection in expansive farms. It overcomes the limitations of existing methods and contributes to more effective livestock management and advancements in agricultural technology.<\/jats:p>","DOI":"10.3390\/ani15121794","type":"journal-article","created":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T07:38:15Z","timestamp":1750232295000},"page":"1794","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery"],"prefix":"10.3390","volume":"15","author":[{"given":"Wenbo","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory of Land Surface Pattern and Simulation, Institute of Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"},{"name":"Jiangxi Key Laboratory of Watershed Ecological Process and Information (Platform No. 2023SSY01051), East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1377-8394","authenticated-orcid":false,"given":"Dongliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Land Surface Pattern and Simulation, Institute of Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Xie","sequence":"additional","affiliation":[{"name":"School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, China"},{"name":"Jiangxi Key Laboratory of Watershed Ecological Process and Information (Platform No. 2023SSY01051), East China University of Technology, Nanchang 330013, China"},{"name":"Key Laboratory of Mine Environmental Monitoring and Improving Around Poyang Lake of Ministry of Natural Resources, East China University of Technology, Nanchang 330013, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2853","DOI":"10.1098\/rstb.2010.0134","article-title":"Livestock production: Recent trends, future prospects","volume":"365","author":"Thornton","year":"2010","journal-title":"Philos. Trans. R. Soc. B Biol. Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.1080\/13658816.2011.554838","article-title":"Geographically weighted principal components analysis","volume":"25","author":"Harris","year":"2011","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_3","first-page":"497","article-title":"Using UAV remote sensing to analyze the population and distribution of large wild herbivores","volume":"22","author":"Quanqin","year":"2021","journal-title":"Natl. Remote Sens. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wang, D., Shao, Q., and Yue, H.J.R.S. (2019). Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): A review. Remote Sens., 11.","DOI":"10.3390\/rs11111308"},{"key":"ref_5","first-page":"53","article-title":"Application Prospects and Challenges of Drones in Animal Husbandry","volume":"39","author":"Cao","year":"2023","journal-title":"Today\u2019s Anim. Husb. Vet. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Gonzalez, L.F., Montes, G.A., Puig, E., Johnson, S., Mengersen, K., and Gaston, K.J. (2016). Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors, 16.","DOI":"10.3390\/s16010097"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.ecoinf.2015.10.008","article-title":"Supporting the annual international black-faced spoonbill census with a low-cost unmanned aerial vehicle","volume":"30","author":"Liu","year":"2015","journal-title":"Ecol. Inform."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"772","DOI":"10.1002\/wsb.596","article-title":"Testing methods for using high-resolution satellite imagery to monitor polar bear abundance and distribution","volume":"39","author":"LaRue","year":"2015","journal-title":"Wildl. Soc. Bull."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Xue, Y., Wang, T., and Skidmore, A.K. (2017). Automatic counting of large mammals from very high resolution panchromatic satellite imagery. Remote Sens., 9.","DOI":"10.3390\/rs9090878"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Torney, C.J., Dobson, A.P., Borner, F., Lloyd-Jones, D.J., Moyer, D., Maliti, H.T., Mwita, M., Fredrick, H., Borner, M., and Hopcraft, J.G.C. (2016). Assessing rotation-invariant feature classification for automated wildebeest population counts. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0156342"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"341","DOI":"10.1016\/j.rse.2017.08.026","article-title":"Detecting animals in African Savanna with UAVs and the crowds","volume":"200","author":"Rey","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"13778","DOI":"10.3390\/s140813778","article-title":"Automated detection and recognition of wildlife using thermal cameras","volume":"14","author":"Christiansen","year":"2014","journal-title":"Sensors"},{"key":"ref_13","unstructured":"W\u00fcthrich, L.M. (2025). Counting Sheep in Aerial Imagery Under Adverse Conditions. [Bachelor\u2019s Thesis, University of Bern]."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"85","DOI":"10.35633\/inmateh-67-08","article-title":"A real-time sheep counting detection system based on machine learning","volume":"67","author":"Deng","year":"2022","journal-title":"INMATEH-Agric. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"107696","DOI":"10.1016\/j.compag.2023.107696","article-title":"A sheep dynamic counting scheme based on the fusion between an improved-sparrow-search YOLOv5x-ECA model and few-shot deepsort algorithm","volume":"206","author":"Cao","year":"2023","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, Z., Liu, Y., Yu, S., Wang, R., Song, Z., Yan, Y., Li, F., Wang, Z., and Tian, F. (2022). Automatic detection method of dairy cow feeding behaviour based on YOLO improved model and edge computing. Sensors, 22.","DOI":"10.3390\/s22093271"},{"key":"ref_17","first-page":"42","article-title":"CSD-YOLOv8s: Dense Sheep Small Target Detection Model Based on UAV Images","volume":"6","author":"Zhi","year":"2024","journal-title":"Smart Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"15515","DOI":"10.1109\/JSEN.2020.3031861","article-title":"Artificial intelligence-based surveillance system for railway crossing traffic","volume":"21","author":"Sikora","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_19","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_20","doi-asserted-by":"crossref","unstructured":"Wang, C.-Y., Bochkovskiy, A., and Liao, H.-Y.M. (2023, January 17\u201324). 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_21","doi-asserted-by":"crossref","unstructured":"Zhu, X., Lyu, S., Wang, X., and Zhao, Q. (2021, January 11\u201317). TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCVW54120.2021.00312"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Li, X., Wei, Y., Li, J., Duan, W., Zhang, X., and Huang, Y. (2024). Improved YOLOv7 algorithm for small object detection in unmanned aerial vehicle image scenarios. Appl. Sci., 14.","DOI":"10.3390\/app14041664"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, J., Huang, Z., Yuan, H., Li, P., Jiang, X., Wang, X., Zhong, C., and Lin, Q. (2023). Improved YOLOv7-based algorithm for detecting foreign objects on the roof of a subway vehicle. Sensors, 23.","DOI":"10.3390\/s23239440"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"25797","DOI":"10.1038\/s41598-024-75809-z","article-title":"A Lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU on YOLOv8","volume":"14","author":"Cai","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_25","unstructured":"Tong, Z., Chen, Y., Xu, Z., and Yu, R. (2023). Wise-IoU: Bounding box regression loss with dynamic focusing mechanism. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Wang, Y., Li, Y., Wang, G., and Liu, X. (2024, January 16\u201322). Multi-scale attention network for single image super-resolution. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPRW63382.2024.00602"},{"key":"ref_27","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_28","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, Seoul, Republic of Korea."}],"container-title":["Animals"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/12\/1794\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:54:08Z","timestamp":1760032448000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-2615\/15\/12\/1794"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,18]]},"references-count":28,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["ani15121794"],"URL":"https:\/\/doi.org\/10.3390\/ani15121794","relation":{},"ISSN":["2076-2615"],"issn-type":[{"value":"2076-2615","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,18]]}}}