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Evaluation of Deep Learning for Automatic Multi-View Face Detection in Cattle

文献类型： 外文期刊

作者： Xu, Beibei 1 ; Wang, Wensheng 1 ; Guo, Leifeng 1 ; Chen, Guipeng 4 ; Wang, Yaowu 5 ; Zhang, Wenju 1 ; Li, Yongfeng 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100086, Peoples R China

2.Minist Agr & Rural Affairs, Informat Ctr, Beijing 100125, Peoples R China

3.Minist Agr & Rural Affairs, Key Lab Agr Big Data, Beijing 100086, Peoples R China

4.Jiangxi Acad Agr Sci, Agr Econ & Informat Inst, Nanchang 330200, Jiangxi, Peoples R China

5.Wageningen Univ, Lab Geoinformat Sci & Remote Sensing, NL-6708 PB Wageningen, Netherlands

关键词： cattle face detection; RetinaNet; deep learning; precision livestock

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2021 年 11 卷 11 期

页码：

收录情况： SCI

摘要： Individual identification plays an important part in disease prevention and control, traceability of meat products, and improvement of agricultural false insurance claims. Automatic and accurate detection of cattle face is prior to individual identification and facial expression recognition based on image analysis technology. This paper evaluated the possibility of the cutting-edge object detection algorithm, RetinaNet, performing multi-view cattle face detection in housing farms with fluctuating illumination, overlapping, and occlusion. Seven different pretrained CNN models (ResNet 50, ResNet 101, ResNet 152, VGG 16, VGG 19, Densenet 121 and Densenet 169) were fine-tuned by transfer learning and re-trained on the dataset in the paper. Experimental results showed that RetinaNet incorporating the ResNet 50 was superior in accuracy and speed through performance evaluation, which yielded an average precision score of 99.8% and an average processing time of 0.0438 s per image. Compared with the typical competing algorithms, the proposed method was preferable for cattle face detection, especially in particularly challenging scenarios. This research work demonstrated the potential of artificial intelligence towards the incorporation of computer vision systems for individual identification and other animal welfare improvements.

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