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
  • [11] ZHAO K, JIN X, JI J, Et al., Individual identification of Holstein dairy cows based on detecting and matching feature points in body images[J], Riosystems Engineering, 181, pp. 128-139, (2019)
  • [12] WANG Xiangyou, LI Yanxing, YANG Zhenyu, Et al., Detection method of clods and stones from impurified potatoes based on improved YOLOv v4 algorithm[J], Transactions of the Chinese Society for Agricultural Machinery, 52, 8, pp. 241-247, (2021)
  • [13] LIU Fang, LIU Yukun, LIN Sen, Et al., Fast recognition method for tomatoes under complex environments based on improved YOLOv [J], Transactions of the Chinese Society for Agricultural Machinery, 51, 6, pp. 229-237, (2020)
  • [14] LIU Xiaogang, FAN Cheng, LI Jianian, Et al., Identification method of strawberry based on convolutional neural network[J], Transactions of the Chinese Society for Agricultural Machinery, 51, 2, pp. 237-244, (2020)
  • [15] LI Zhenbo, LI Meng, WU Yufeng, Et al., Evaluation method of iced pomfret freshness based on improved SSD [J], Transactions of the Chinese Society for Agricultural Machinery, 52, pp. 472-481, (2021)
  • [16] CHENG Yi, ZHANG Yu, LI Baoquan, Traffic sign detection algorithm based on improved CenterNet [J], Journal of Signal Processing, 38, 3, pp. 511-518, (2022)
  • [17] YANG Shuqin, LIU Jiangchuan, XU Keke, Et al., Improved CenterNet based maize tassel recognition for UAV remote sensing image [J], Transactions of the Chinese Society for Agricultural Machinery, 52, 9, pp. 206-212, (2021)
  • [18] YAN Jianwei, ZHAO Yuan, ZHANG Lewei, Et al., Recognition of Rosa roxbunghii in natural environment based on improved Faster RCNN, Transactions of the CSAE, 35, 18, pp. 143-150, (2019)
  • [19] HE Dongjian, LIU Jianmin, XIONG Hongting, Et al., Individual identification of dairy cows based on improved YOLOv v3[J], Transactions of the Chinese Society for Agricultural Machinery, 51, 4, pp. 250-260, (2020)
  • [20] XIAO J, LIU G, WANG K, Et al., Cow identification in free-stall barns based on an improved Mask R -CNN and an SVM [J], Computers and Electronics in Agriculture, 194, (2022)
123