An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery
Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage–livestock balance assessment. These tasks are integral to the modernization and upgrading of the livestock industry and the sustainable develo...
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MDPI AG
2025-06-01
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| author | Wenbo Chen Dongliang Wang Xiaowei Xie |
| author_facet | Wenbo Chen Dongliang Wang Xiaowei Xie |
| author_sort | Wenbo Chen |
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| description | Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage–livestock 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. |
| format | Article |
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| institution | Kabale University |
| issn | 2076-2615 |
| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-e2afbe784b9f4188a4cc9947fad82d2e2025-08-20T03:26:52ZengMDPI AGAnimals2076-26152025-06-011512179410.3390/ani15121794An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle ImageryWenbo Chen0Dongliang Wang1Xiaowei Xie2Key Laboratory of Land Surface Pattern and Simulation, Institute of Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaKey Laboratory of Land Surface Pattern and Simulation, Institute of Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaSchool of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang 330013, ChinaLivestock population surveys are crucial for grassland management tasks such as health and epidemic prevention, grazing prohibition, rest grazing, and forage–livestock 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.https://www.mdpi.com/2076-2615/15/12/1794unmanned aerial vehicle (UAV)deep learningobject detectionlivestock population surveys |
| spellingShingle | Wenbo Chen Dongliang Wang Xiaowei Xie An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery Animals unmanned aerial vehicle (UAV) deep learning object detection livestock population surveys |
| title | An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery |
| title_full | An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery |
| title_fullStr | An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery |
| title_full_unstemmed | An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery |
| title_short | An Efficient Algorithm for Small Livestock Object Detection in Unmanned Aerial Vehicle Imagery |
| title_sort | efficient algorithm for small livestock object detection in unmanned aerial vehicle imagery |
| topic | unmanned aerial vehicle (UAV) deep learning object detection livestock population surveys |
| url | https://www.mdpi.com/2076-2615/15/12/1794 |
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