摘要
羊群羊只计数是畜牧生产过程中较为耗时费力的环节,在草原畜牧业中,统计羊只数量有助于过度放牧监测和草原生态评估。采用无人机(UAV)获取航拍图像,制作了羊群密集计数(USC)数据集,为羊群密集计数研究提供数据支持。在USC数据集上,对MCNN、CSRNet、SFANet、Bayesian Loss网络模型进行了实验。实验结果表明:由于遮挡,羊群的不规则分布,羊群大小、形状、密度等方面的巨大变化,MCNN、CSRNet、SFANet模型将假设的高斯核应用于点标注计算真值密度图,很难达到高质量;而Bayesian Loss模型提出的Bayesian损失函数对每只羊标注点的计数期望进行监督,取得了较好的计数效果,密度图平均绝对误差(MAE)为3.56,均方误差(MSE)为5.46,平均相对误差(MRE)为1.86%,可为草原羊群密集计数提供有益参考。
It is a time-consuming and laborious task to manually count the number of sheep in the process of pastoral livestock production.Counting the number of sheep in grassland animal husbandry has been helpful for overgrazing monitoring and grassland ecology assessment.A unmanned aerial vehicle(UAV)is used to obtain aerial images,and a UAV sheep counting(USC)dataset is made to provide data support for the study of flock dense counting.Based on the USC dataset,experiments with network models,included MCNN,CSRNet,SFANet,and Bayesian Loss are carried out.The experimental results show that due to occlusion,irregular distribution of sheep,great changes in sheep size,shape,and density,MCNN,CSRNet,and SFANet models apply the assumed Gaussian kernel to point labeling to calculate the truth density map,which are difficult to achieve high quality.However,the Bayesian loss function proposed by Bayesian Loss model supervises the counting expectation of each sheep’s labeling points.The average absolute error(MAE)of the density map obtained by the Bayesian Loss model is 3.56,the mean square error(MSE)is 5.46,and the average relative error(MRE)is 1.86%,which provides a useful reference for the dense counting of grassland sheep.
作者
赵建敏
李雪冬
李宝山
Zhao Jianmin;Li Xuedong;Li Baoshan(School of lnformatiou Engineering,Imner Mongolia University of Science&Technology,Baotou,lner Mongolia 014010,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2021年第22期212-221,共10页
Laser & Optoelectronics Progress
基金
内蒙古自然科学基金(2019MS06021,2019LH06006)
内蒙古自治区科技重大专项(2019ZD025)。
关键词
图像处理
卷积神经网络
密集计数
羊群计数
密度估计
无人机
image processing
convolutional neural network
dense counting
sheep counting
density estimation
unmanned aerial vehicle
