江西科学2025，Vol.43Issue(2)：253-261,9.DOI:10.13990/j.issn1001-3679.2025.02.006
LSNet——一种用于无人机图像中小型牲畜目标检测的高效算法
LSNet——An Efficient Algorithm for Small Livestock Object Detection in UAV Imagery
摘要
Abstract
Livestock population surveys are crucial for grassland management tasks such as health and epidemic prevention,grazing prohibition,rotational grazing,and forage-live-stock balance assessment.These surveys are directly related to modernization transformation and upgrading of the livestock industry and the sustainable development of grasslands.When using UAVs for livestock population surveys,it's inevitable to encounter issues such as small and densely packed livestock that often lead to recognition errors.To address this is-sue,the paper proposes an efficient algorithm called Livestock Network(LSNet).The algo-rithm 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,a Large Kernel Global Attention Spatial Pyramid Pooling(LKGSPP)module is proposed.Additionally,a bidirec-tional feature pyramid network(BiFPN)structure is integrated to enhance the fusion effec-tiveness of multi-scale feature maps in UAV images.Furthermore,a dataset of grazing livestock for deep learning using UAV images was developed from the Prairie Chenbarhu Banner in Hulunbuir,Inner Mongolia.The experimental results demonstrate that the pro-posed module significantly improves the detection accuracy for small livestock objects,with the mean Average Precision(mAP)increasing by 1.78%compared to YOLOv7.Thus,the LSNet method is an effective tool for detecting grazing livestock in UAV imagery.关键词
无人机/YOLOv7/目标检测/牲畜种群数量调查Key words
unmanned aerial vehicle(UAV)/YOLOv7/deep learning/object detection/livestock population surveys分类
测绘与仪器引用本文复制引用
谌文博,谢小伟,王东亮..LSNet——一种用于无人机图像中小型牲畜目标检测的高效算法[J].江西科学,2025,43(2):253-261,9.基金项目
国家重点研发计划项目(2021YFD1300501) （2021YFD1300501）
东华理工大学江西省数字国土重点实验室开放基金项目(DLLJ202206) （DLLJ202206）
江西省研究生创新基金项目(YC2023-S560). （YC2023-S560）
