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

Beibei Xu, Wensheng Wang, Leifeng Guo, Guipeng Chen, Yaowu Wang, Wenju Zhang and Yongfeng Li
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Beibei Xu: Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Wensheng Wang: Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Leifeng Guo: Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Guipeng Chen: Agricultural Economics and Information Institute, Jiangxi Academy of Agriculture Sciences, Nanchang 330200, China
Yaowu Wang: Laboratory of Geo-Information Science and Remote Sensing, Wageningen University, 6708 PB Wageningen, The Netherlands
Wenju Zhang: Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China
Yongfeng Li: Agricultural Information Institute, Chinese Academy of Agriculture Sciences, Beijing 100086, China

Agriculture, 2021, vol. 11, issue 11, 1-15

Abstract: 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.

Keywords: cattle face detection; RetinaNet; deep learning; precision livestock (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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