An Improved Single Shot Multibox Detector Method Applied in Body Condition Score for Dairy Cows

Animals

卷:9

Xuanjiang Yang¹  Jian Zhang¹  Xiaoping Huang¹  Daoling Shi²  Xiaorun Wang³  Zelin Hu⁴ 

[1] Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei 230031, China;

[2] School of Electronic and Communication Engineering, Anhui Xinhua University, Hefei 230088, China;

[3] School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China;

[4] University of Science and Technology of China, Hefei 230026, China;

关键词: body condition score (BCS);    sing shot multi-box detector (SSD);    non-contact sensing;    machine vision;    dairy cow;   

DOI  :  10.3390/ani9070470

来源: DOAJ

【 摘 要 】

Body condition scores (BCS) is an important parameter, which is in high correlation with the health status of a dairy cow, metabolic disorder and milk composition during the production period. To evaluate BCS, the traditional methods rely on veterinary experts or skilled staff to look at a cow and touch it. These methods have low efficiency especially on large-scale farms. Computer vision methods are widely used but there are some improvements to increase BCS accuracy. In this study, a low cost BCS evaluation method based on deep learning and machine vision is proposed. Firstly, the back-view images of the cows are captured by network cameras, resulting in 8972 images that constituted the sample data set. The camera is a common 2D camera, which is cheaper and easier to install compared with 3D cameras. Secondly, the key body parts such as tails, pins and rump in the images were labeled manually, the Sing Shot multi-box Detector (SSD) method was used to detect the tail and evaluate the BCS. Inspired by DenseNet and Inception-v4, a new SSD was introduced by changing the network connection method of the original SSD. Finally, the experiments show that the improved SSD method can achieve 98.46% classification accuracy and 89.63% location accuracy, and it has: (1) faster detection speed with 115 fps; (2) smaller model size with 23.1 MB compared to original SSD and YOLO-v3, these are significant advantages for reducing hardware costs.

【 授权许可】

Unknown