The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
우수사육관리와 축산보험의 기초로서 젖소에 대한 개별인정은 축산관리 분야에서 중요한 이슈입니다. 쉽게 떨어뜨리고 위조하는 등 전통적인 소 식별 방법의 한계로 인해 더 이상 현대 지능형 목초지 관리의 요구를 충족할 수 없습니다. 최근에는 컴퓨터 비전 기술이 발전하면서 얼굴인식 분야에서도 딥러닝이 빠르게 발전하고 있습니다. 인식 정확도는 인간의 얼굴 인식 수준을 뛰어넘어 생산 환경에서 널리 사용되고 있습니다. 그러나 젖소 등 대형 가축의 안면인식에 관한 연구는 계속해서 발전되고 개선될 필요가 있다. 잔차 네트워크(Residual Network) 아이디어에 따라, 본 논문에서는 젖소 얼굴 이미지를 기반으로 개별 젖소 인식을 위한 개선된 합성곱 신경망(Res_5_2Net) 방법을 제안합니다. 자체 구축한 소 얼굴 데이터베이스(3012개의 훈련 세트, 1536개의 테스트 세트)의 인식 정확도는 94.53%에 도달할 수 있습니다. 실험 결과는 젖소 식별 효율성이 효과적으로 향상되었음을 보여줍니다.
Zhi WENG
Inner Mongolia Agricultural University,Inner Mongolia University
Longzhen FAN
Inner Mongolia University
Yong ZHANG
Inner Mongolia Agricultural University
Zhiqiang ZHENG
Inner Mongolia University
Caili GONG
Inner Mongolia Agricultural University,Inner Mongolia University
Zhongyue WEI
Inner Mongolia University
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부
Zhi WENG, Longzhen FAN, Yong ZHANG, Zhiqiang ZHENG, Caili GONG, Zhongyue WEI, "Facial Recognition of Dairy Cattle Based on Improved Convolutional Neural Network" in IEICE TRANSACTIONS on Information,
vol. E105-D, no. 6, pp. 1234-1238, June 2022, doi: 10.1587/transinf.2022EDP7008.
Abstract: As the basis of fine breeding management and animal husbandry insurance, individual recognition of dairy cattle is an important issue in the animal husbandry management field. Due to the limitations of the traditional method of cow identification, such as being easy to drop and falsify, it can no longer meet the needs of modern intelligent pasture management. In recent years, with the rise of computer vision technology, deep learning has developed rapidly in the field of face recognition. The recognition accuracy has surpassed the level of human face recognition and has been widely used in the production environment. However, research on the facial recognition of large livestock, such as dairy cattle, needs to be developed and improved. According to the idea of a residual network, an improved convolutional neural network (Res_5_2Net) method for individual dairy cow recognition is proposed based on dairy cow facial images in this letter. The recognition accuracy on our self-built cow face database (3012 training sets, 1536 test sets) can reach 94.53%. The experimental results show that the efficiency of identification of dairy cows is effectively improved.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2022EDP7008/_p
부
@ARTICLE{e105-d_6_1234,
author={Zhi WENG, Longzhen FAN, Yong ZHANG, Zhiqiang ZHENG, Caili GONG, Zhongyue WEI, },
journal={IEICE TRANSACTIONS on Information},
title={Facial Recognition of Dairy Cattle Based on Improved Convolutional Neural Network},
year={2022},
volume={E105-D},
number={6},
pages={1234-1238},
abstract={As the basis of fine breeding management and animal husbandry insurance, individual recognition of dairy cattle is an important issue in the animal husbandry management field. Due to the limitations of the traditional method of cow identification, such as being easy to drop and falsify, it can no longer meet the needs of modern intelligent pasture management. In recent years, with the rise of computer vision technology, deep learning has developed rapidly in the field of face recognition. The recognition accuracy has surpassed the level of human face recognition and has been widely used in the production environment. However, research on the facial recognition of large livestock, such as dairy cattle, needs to be developed and improved. According to the idea of a residual network, an improved convolutional neural network (Res_5_2Net) method for individual dairy cow recognition is proposed based on dairy cow facial images in this letter. The recognition accuracy on our self-built cow face database (3012 training sets, 1536 test sets) can reach 94.53%. The experimental results show that the efficiency of identification of dairy cows is effectively improved.},
keywords={},
doi={10.1587/transinf.2022EDP7008},
ISSN={1745-1361},
month={June},}
부
TY - JOUR
TI - Facial Recognition of Dairy Cattle Based on Improved Convolutional Neural Network
T2 - IEICE TRANSACTIONS on Information
SP - 1234
EP - 1238
AU - Zhi WENG
AU - Longzhen FAN
AU - Yong ZHANG
AU - Zhiqiang ZHENG
AU - Caili GONG
AU - Zhongyue WEI
PY - 2022
DO - 10.1587/transinf.2022EDP7008
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E105-D
IS - 6
JA - IEICE TRANSACTIONS on Information
Y1 - June 2022
AB - As the basis of fine breeding management and animal husbandry insurance, individual recognition of dairy cattle is an important issue in the animal husbandry management field. Due to the limitations of the traditional method of cow identification, such as being easy to drop and falsify, it can no longer meet the needs of modern intelligent pasture management. In recent years, with the rise of computer vision technology, deep learning has developed rapidly in the field of face recognition. The recognition accuracy has surpassed the level of human face recognition and has been widely used in the production environment. However, research on the facial recognition of large livestock, such as dairy cattle, needs to be developed and improved. According to the idea of a residual network, an improved convolutional neural network (Res_5_2Net) method for individual dairy cow recognition is proposed based on dairy cow facial images in this letter. The recognition accuracy on our self-built cow face database (3012 training sets, 1536 test sets) can reach 94.53%. The experimental results show that the efficiency of identification of dairy cows is effectively improved.
ER -