Abstract
In smart livestock, precision livestock systems require efficient and safe non-contact cattle identification methods in daily operation and management. In this paper, we focus on lightweight Convolutional Neural Network (CNN) based cattle face identification in natural background. Particularly, we first construct a fine-grained cattle recognition dataset with natural background. Then, we propose a lightweight CNN model MobiCFNet, containing a two-stage method that can realize one-shot cattle recognition. Finally, a series of experiments are conducted to validate the effectiveness of our proposed network .
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References
Cai, C., Li, J.: Cattle face recognition using local binary pattern descriptor. In: 2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference, pp. 1–4. IEEE (2013)
Fosgate, G., Adesiyun, A., Hird, D.: Ear-tag retention and identification methods for extensively managed water buffalo (Bubalus bubalis) in Trinidad. Prev. Vet. Med. 73(4), 287–296 (2006)
Howard, A.G., et al.: MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 (2017)
Hu, X., Sun, L., Zhou, Y., Ruan, J.: Review of operational management in intelligent agriculture based on the internet of things. Front. Eng. Manage. 7(3), 309–322 (2020)
Ismail, W.N., Hassan, M.M., Alsalamah, H.A., Fortino, G.: CNN-based health model for regular health factors analysis in internet-of-medical things environment. IEEE Access 8, 52541–52549 (2020)
Kaixuan, Z., Dongjian, H.: Recognition of individual dairy cattle based on convolutional neural networks. Trans. Chin. Soc. Agric. Eng. 31(5), 181–187 (2015)
Kim, W., Cho, Y.B., Lee, S.: Thermal sensor-based multiple object tracking for intelligent livestock breeding. IEEE Access 5, 27453–27463 (2017)
Kumar, S., et al.: Deep learning framework for recognition of cattle using muzzle point image pattern. Measurement 116, 1–17 (2018)
Kumar, S., Singh, S.K., Dutta, T., Gupta, H.P.: A fast cattle recognition system using smart devices. In: Proceedings of the 24th ACM International Conference on Multimedia, pp. 742–743 (2016)
Kumar, S., Singh, S.K., Singh, R., Singh, A.K.: Animal Biometrics. Springer, Singapore (2017). https://doi.org/10.1007/978-981-10-7956-6
Manoj, S., Rakshith, S., Kanchana, V.: Identification of cattle breed using the convolutional neural network. In: 2021 3rd International Conference on Signal Processing and Communication (ICPSC), pp. 503–507. IEEE (2021)
Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., Clark, C.: Individual cattle identification using a deep learning based framework. IFAC-PapersOnLine 52(30), 318–323 (2019)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: MobileNetV2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
Tang, Q., et al.: Food traceability systems in China: the current status of and future perspectives on food supply chain databases, legal support, and technological research and support for food safety regulation. Biosci. Trends 9(1), 7–15 (2015)
Wang, H., et al.: CosFace: large margin cosine loss for deep face recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5265–5274 (2018)
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Qiao, L., Geng, Y., Zhang, Y., Zhang, S., Xu, C. (2022). MobiCFNet: A Lightweight Model for Cattle Face Recognition in Nature. In: Shi, Z., Jin, Y., Zhang, X. (eds) Intelligence Science IV. ICIS 2022. IFIP Advances in Information and Communication Technology, vol 659. Springer, Cham. https://doi.org/10.1007/978-3-031-14903-0_41
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DOI: https://doi.org/10.1007/978-3-031-14903-0_41
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