Cattle Body Intelligent Measurement based on Improved CenterNet
Main Article Content
Abstract
Traditional cattle body measurement methods have limitations such as manual contact measurement and low efficiency. To improve the efficiency of measuring cattle bodies and reduce labor costs, an improved intelligent measurement network based on CenterNet was proposed. In this study, we used the DenseNet-100 to replace the ResNet-101 to alleviate the vanishing gradient problem. We also improved the cattle body size measurement efficiency by reducing the network parameters. In addition, we conducted a comparative experiment on the cattle posture dataset collected by ourselves to verify the feasibility of the network. The experiment confirmed that the proposed intelligent measurement based on improved CenterNet outperforms the traditional networks and other advanced object measurement networks in accuracy and measurement efficiency, and can effectively solve most of the error problems caused by manual measurement. Besides, our network has good applicability and strong stability, which can meet the requirements of the evaluation of cattle body size measurement indicators.
Article Details
This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License.
References
B. Kristijan, B. David, P. Tomislav, et al, "A review of body measurement using 3D scanning", IEEE Access, vol.9, pp.67281-67301, April, 2021.
W. Erda, F. S. Daniel, P. M. Patrick, et al, "Channel characterization of magnetic human body communication", IEEE Transactions on Biomedical Engineering, vol. 69, no.2, pp.569-579, February, 2022.
T. Gao, F. M. Li, Y. Q. Yang, et al, “Gentle adaboost based cattle detection,” Application Research of Computers, vol.33, no.11, pp. 3495-3498, 2016.
S. Mathavaraj, Eric A. Butcher, "SE(3)-constrained extended kalman filtering for rigid body pose estimation", IEEE Transactions on Aerospace and Electronic Systems, vol.58. no.3, 2482-2492, June, 2022.
W. Shi, S. Q. Zhang, “Application of edge-based image segmentation in cattle body measurement,” Digital Technology and Application, vol.32, no.2, pp.48-51, 2020.
Y. M. Hu, S. M. Zhang, “Dairy cattle body measurement based on machine vision,” Electronic Measurement Technology, vol.43, no.20, pp.115-120, 2020.
L. Zhu, W. Zhang, Q. Li, et al, “Measuring system of sheep body size based on embedded machine vision,” Computer Measurement & Control, vol.22, no.8, pp.2396-2398, 2014.
N. P. Tomov, V. Kokalanov, S. Koceski, "Deep learning-based real-time body measurements using device camera", 12th Mediterranean Conference on Embedded Computing, Budva, June 2023.
J. M. Zhao, Z. X. Zhao, Q. Li, “Measurement method of sheep body size based on Kinect sensor,” Jiangsu Agricultural Sciences, vol.43, no.11, pp.495-499, 2015．
Y. Q. Zhou, H. R. Xue, X. H, “Jiang, et al. Non-contact measurement of sheep body size based on multi-scale Retinex image enhancement,” Journal of China Agricultural University, vol.23, no.9, pp.156-165, 2018．
A. Hirata, T. Miyazawa, R. Uematsu, et al, "Body core temperature estimation using new compartment model with vital data from wearable devices", IEEE Access, vol.9, pp.124452-124462, September 2021.
C. Romano, D. Formica, E. Schena, et al, "Investigation of body locations for cardiac and respiratory monitoring with skin-interfaced inertial measurement unit sensors", IEEE Sensors Journal, vol.23, no.7, pp.7806-7815, April 2023.
J. M. Zhao, B. Wen, Q. Li, “A bull body ruler measurement system based on Mask R-CNN,” Animal Husbandry & Veterinary Medicine, vol.53, no.5, pp.42-48, 2021.
S. Vandermeeren, H. Steendam, "Deep-learning-based step detection and step length estimation with a handheld IMU", IEEE Sensors Journal, vol.22, no.24, December, 2022.
J. Y. Xu, A. J. Xu, S. Y. Zhou, et al, “Research on the algorithm of curved body size measurement of pig based on Kinect camera,” Journal of Northeast Agricultural University, vol.52, no.9, pp.77-85, 2021.
M. Everingham, L. V. Gool, C. K. I. Williams, et al, “The pascal visual object classes (VOC) challenge,” International Journal of Computer Vision, vol.88, no.2, pp.303-338, 2010.
K. Duan, S. Bai, L. Xie, et al, “CenterNet: Keypoint triplets for object detection,” In Proceedings of the IEEE International Conference on Computer Vision, Seoul, Korea, pp.6568–6577, October, 2019.
A. Boonkong, K. Khampitak, D. Hormdee, "Performing and evaluation of deep learning models for uterus detection on soft-tissue cadavers in laparoscopic gynecology", IEEE Engineering in Medicine and Biology Society Section, vol.11, pp.72027-72036, July, 2023.
X. Wang, S. Kang, W. D. Zhu, “Defect detection of laminated surface in the automated fiber placement process based on improved CenterNet,” Infrared and Laser Engineering, vol.50, no.10, pp.202-212, 2021.
S. Aladhadh, R. Mahum, “Knee osteoarthritis detection using an improved CenterNet with pixel-wise voting scheme,” IEEE Access, PP.22283–22296, February, 2023.