Estrus Behavior Recognition of Dairy Cows Based on Improved YOLO v3 Model

被引：0

作者：

Wang S. ^{[1
,2
]}

He D. ^{[1
,2
]}

机构：

[1] College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling

[2] Key Laboratory of Agricultural Internet of Things, Ministry of Agriculture and Rural Affairs, Yangling

来源：

Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2021年 / 52卷 / 07期

关键词：

Anchor optimization; Dairy cow estrus; DenseBlock; Loss function optimization; Mounting behavior; YOLO v3;

D O I：

10.6041/j.issn.1000-1298.2021.07.014

中图分类号：

学科分类号：

摘要：

Aiming to improve the detection accuracy and speed of estrus behavior of dairy cows in a complex scene, a method of recognizing estrus behavior of dairy cows based on improved YOLO v3 model was proposed. To solve the problem of the cows' size was inconsistent with the size of the object in the COCO dataset, which caused the original anchors were not applicable, new anchors were obtained by clustering new data sets and optimized by using linear expansion. As cows with a big size, the small difference between individuals and associations between behaviors, which was difficult to distinguish, a DenseBlock structure was introduced to the feature extraction network of YOLO v3 model to improve its detection performance on the large objects. Considered that the original bounding box loss function of YOLO v3 model was not invariant to the object scale, the FIoU and the center distance Dc of two boxes were used as the measuring method, and a new bounding box loss function was proposed to make it scale-invariant. Totally 50 images were extracted each from 96 video mounting behavior clips of dairy cows, according to the uncertainty position of cows' mounting behavior in the active area and the character of the light changing of the active area, horizontally flipped, rotated ±15° and random brightness enhancement (decrease) were applied on them for data augmentation. The augmented data was divided into three parts as training sets, validation sets, and test sets, training sets and validation sets were used to train the improved model and the best training model was chosen as dairy cow estrus behavior recognition model with the indicators F1, mAP, accuracy rate P, and recall rate R. The experiment on test sets showed that the accuracy rate of the model was 99.15%, the recall rate was 97.62%, and the processing speed reached 31 f/s, which could accurately and real-time identify cows' estrus behavior in a complex breeding environment under all weather. The research could also provide a reference for other large livestock behavior recognition. © 2021, Chinese Society of Agricultural Machinery. All right reserved.

引用

页码：141 / 150

页数：9

共 27 条

[11] XU B, WANG W, FALZON G, Et al., Automated cattle counting using Mask R-CNN in quadcopter vision system, Computers and Electronics in Agriculture, 171, (2020)
[12] ZHAO K, BEWLEY J M, HE D, Et al., Automatic lameness detection in dairy cattle based on leg swing analysis with an image processing technique, Computers and Electronics in Agriculture, 148, pp. 226-236, (2018)
[13] HE Dongjian, LIU Jianmin, XIONG Hongting, Et al., Individual identification of dairy cows based on improved YOLO v3, Transactions of the Chinese Society for Agricultural Machinery, 51, 4, pp. 250-260, (2020)
[14] YANG Qiumei, XIAO Deqin, ZHANG Genxing, Automatic pig drinking behavior recognition with machine vision, Transactions of the Chinese Society for Agricultural Machinery, 49, 6, pp. 232-238, (2018)
[15] KANG Xi, ZHANG Xudong, LIU Gang, Et al., Hoof location method of lame dairy cows based on machine vision, Transactions of the Chinese Society for Agricultural Machinery, 50, pp. 276-282, (2019)
[16] PARK S, JUNG D, SANG H M, Et al., Development of vocal recording and analysis system for laying hens and cow based-on cloud-computing, 2019 ASABE Annual International Meeting, (2019)
[17] LIU Zhongchao, HE Dongjian, Recognition method of cow estrus behavior based on convolutional neural network, Transactions of the Chinese Society for Agricultural Machinery, 50, 7, pp. 186-193, (2019)
[18] WANG Shaohua, HE Dongjian, LIU Dong, Automatic recognition method of dairy cow estrus behavior based on machine vision, Transactions of the Chinese Society for Agricultural Machinery, 51, 4, pp. 241-249, (2020)
[19] LI Dan, ZHANG Kaifeng, LI Xingjian, Et al., Mounting behavior recognition for pigs based on Mask R-CNN, Transactions of the Chinese Society for Agricultural Machinery, 50, pp. 261-266, (2019)
[20] ZHAO Kaixuan, HE Dongjian, Recognition of individual dairy cattle based on convolutional neural networks, Transactions of the CSAE, 31, 5, pp. 181-187, (2015)

← 1 2 3 →