Feature Mask-based Local Occlusion Cattle Face Recognition Method

被引：0
作者：
Qi, Yongsheng [1 ]
Zhang, Xinze [1 ]
Zhang, Jiaying [1 ,2 ]
Liu, Liqiang [1 ,2 ]
Li, Yongting [1 ,2 ]
机构：
[1] College of Electric Power, Inner Mongolia University of Technology, Hohhot
[2] Intelligent Ener. Technol. and Equip. Eng. Res. Ctr. of Colleges and Universities in the Inner Mongolia Autonomous Reg., Hohhot
来源：
Nongye Jixie Xuebao/Transactions of the Chinese Society for Agricultural Machinery | 2024年 / 55卷 / 11期
关键词：
dual- branch structure; image segmentation; multi-level mask learning; occluded cattle face recognition;
D O I：
10.6041/j.issn.1000-1298.2024.11.010
中图分类号：
学科分类号：
摘要：
With the rapid development of intelligent animal husbandry, bovine face recognition has become the key to intelligent cattle breeding, but the problem of bovine face occlusion in practical application scenarios is more serious, which brings challenges to the performance of the recognition system. To solve this problem, a two-branch network structure based on occluder-assisted bovine face recognition was proposed. Firstly, an improved lightweight U - Net occlusion segmentation model was designed. By adding deep separable convolution and multi-scale mixing pool module, the occlusion segmentation performance of the segmentation network was effectively improved. Secondly, in order to better attenuate the influence of occlusions on bovine face recognition performance, a multilevel mask generation unit was introduced, and masks corresponding to different stages of the recognition network were constructed with different levels of occlusions as input. The damaged feature information caused by occlusions was effectively eliminated in each stage of feature extraction through mask operation. Finally, for the validity and real-time performance of the detection algorithm, the algorithm was verified on the self-made data set, and compared with a variety of recent typical recognition algorithms. The experimental results showed that the proposed algorithm had an average accuracy of 86. 34% on the blocked cow face data set, and the recognition speed was 54 f/s. Compared with the single-scale mask, the average accuracy of multistage mask was improved by 2.02 percentage points, and the recognition effect was better than that of the comparison network under different degrees of occlusion. © 2024 Chinese Society of Agricultural Machinery. All rights reserved.
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页码：93 / 102
页数：9
相关论文
共 31 条
  • [1] MAHMUD M S, ZAHID A, DAS A K, Et al., A systematic literature review on deep learning applications for precision cattle farming[J], Computers and Electronics in Agriculture, 187, pp. 1-16, (2021)
  • [2] YANG Mei, ZHAO Jianmin, Design of cow face recognition system for insurance business based on three-dimensional loss algorithm [J], Journal of Optoelectronics-Laser, 33, 8, pp. 831-839, (2022)
  • [3] MANCUSO D, CASTA GNOLO G, PORTO SMC., Cow behavioural activities in extensive farms
  • [4] challenges of adopting automatic monitoring systems, Sensors, 23, 8, (2023)
  • [5] HUANG Xiaoping, FENG Tao, GUO Yangyang, Et al., Lightweight dairy cow body condition scoring method based on improved YOLO v5s[J], Transactions of the Chinese Society for Agricultural Machinery, 54, 6, pp. 287-296, (2023)
  • [6] WANG Zheng, XU Xingshi, HUA Zhixin, Et al., Lightweight recognition for the oestrus behavior of dairy cows combining YOLO v5n and channel pruning, Transactions of the CSAE, 38, 23, pp. 130-140, (2022)
  • [7] WILLIAMS L R, FOX D R, BISHOP-HURLEY G J, Et al., Use of radio frequency identification (RFID) technology to record grazing beef cattle water point use[J], Computers and Electronics in Agriculture, 156, pp. 193-202, (2019)
  • [8] KUMAR S, SINGH S K., Cattle recognition
  • [9] a new frontier in visual animal biometrics research, Proceedings of the National Academy of Sciences India Section, 90, 4, pp. 689-708, (2020)
  • [10] WANG Jing, LI Weiran, LIU Yeqiang, Et al., Review of vision counting methods and applications for farmed animals, Transactions of the Chinese Society for Agricultural Machinery, 54, pp. 315-329, (2023)
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