Individual Identification Method of Lying Cows Based on MSRCP and Improved YOLO v4 Model

被引：0
|
作者：
Si Y. [1 ,2 ]
Xiao J. [1 ,2 ]
Liu G. [3 ]
Wang K. [1 ,2 ]
机构：
[1] College of Information Science and Technology, Hebei Agricultural University, Baoding
[2] Key Laboratory of Agricultural Big Data of Hebei Province, Baoding
[3] Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture and Rural Affairs, China Agricultural University, Beijing
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2023年 / 54卷 / 01期
关键词：
improved YOLO v4; individual identification; lying cows; machine vision;
D O I：
10.6041/j.issn.1000-1298.2023.01.024
中图分类号：
学科分类号：
摘要：
The lying rate of dairy cows can reflect the comfort and health of dairy cows. The individual identification of lying cows is the basis of automatic monitoring of lying rate. A method based on the improved YOLO v4 model to identify individual lying cows in an unconstrained barn environment was proposed. Firstly, in order to realize accurate individual identification of lying cows throughout the day, MSRCP algorithm was used to enhance the images from 18 ;00 to 07 ;00 the next day, which improved the image quality in low light environment. Secondly, the RFB - s structure was integrated into the backbone network of YOLO v4 model to increase the robustness of the model to the changes of cow body patterns. Finally, in order to improve the identification accuracy rate of cows with similar patterns, the non-maximum suppression (NMS) algorithm of YOLO v4 model was improved. The experiment of cow individual identification was carried out on the image data set of 72 cows. The results showed that the precision, recall, mAP, and Fl values of the improved YOLO v4 were 97. 84%, 93. 68%, 96. 87%, and 95. 71%, respectively. The improved YOLO v4 model was compared with the YOLO v4 model, the precision, recall, mAP and Fl values of the improved YOLO v4 were increased by 4.66 percentage points, 3.07 percentage points, 4.20 percentage points and 3.83 percentage points, respectively, without reducing the processing speed. The mAP of the improved YOLO v4 was higher than that of YOLO v5, SSD, CenterNet and Faster R - CNN by 8. 52 percentage points, 15. 22 percentage points, 12. 18 percentage points and 1.55 percentage points, respectively. The method can provide an effective technical support for the health monitoring of dairy cows in precision dairy farming. © 2023 Chinese Society of Agricultural Machinery. All rights reserved.
引用
收藏
页码：243 / 262
页数：19
相关论文
共 28 条
  • [1] SEPULVEDA-VARAS P, WEARY D M, KEYSERLINGK M., Lying behavior and postpartum health status in grazing dairy cows, Journal of Dairy Science, 97, 10, pp. 6334-6343, (2014)
  • [2] PORTO S, ARCIDIACONO C, ANGUZZA U, Et al., A computer vision-based system for the automatic detection of lying behaviour of dairy cows in free-stall barns, Biosystems Engineering, 115, 2, pp. 184-194, (2013)
  • [3] WANG Jun, ZHANG Haiyang, ZHAO Kaixuan, Et al., Cow movement behavior classification based on optimal binary decision-tree classification model, Transactions of the CSAE, 34, 18, pp. 202-210, (2018)
  • [4] LIU Zhongchao, HE Dongjian, Recognition method of cow estrus behavior based on convolutional neural network [J], Transactions of the Chinese Society for Agricultural Machinery, 50, 7, pp. 186-193, (2019)
  • [5] HE Dongjian, LIU Dong, ZHAO Kaixuan, Review of perceiving animal information and behavior in precision livestock farming [J], Transactions of the Chinese Society for Agricultural Machinery, 47, 5, pp. 231-244, (2016)
  • [6] XU Reibei, WANG Wensheng, GUO Leifeng, Et al., A review and future prospects on cattle recognition based on non-contact identification [J], Journal of Agricultural Science and Technology, 22, 7, pp. 79-89, (2020)
  • [7] CAI Wenqing, LIANG Rin, Research and implementation of the safety of raw milk traeeability management perspective based on RFID technology [J], Hubei Agriculture Science, 50, 7, pp. 1473-1475, (2011)
  • [8] KUSAKUNNIRAN W, CHAIVIROONJAROEN T., Automatic cattle identification based on multi-channel LRP on muzzle images [C], IEEE International Conference on Sustainable Information Engineering and Technology (SIET), pp. 1-5, (2018)
  • [9] CAI C, LI J., Cattle face recognition using local binary pattern descriptor[C], Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1-4, (2013)
  • [10] HU H, DAI R, SHEN W, Et al., Cow identification based on fusion of deep parts features, Riosystems Engineering, 192, pp. 245-256, (2020)
123