[1] Thorup V M, Munksgaard L, Robert P E et al. Lameness detection via leg-mounted accelerometers on dairy cows on four commercial farms[J]. Animal, 9, 1704-1712(2015).
[2] Maertens W, Vangeyte J, Baert J et al. Development of a real time cow gait tracking and analysing tool to assess lameness using a pressure sensitive walkway: the GAITWISE system[J]. Biosystems Engineering, 110, 29-39(2011).
[3] Yang Q M, Xiao D Q, Zhang G X. Automatic pig drinking behavior recognition with machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 232-238(2018).
[4] Kang X, Zhang X D, Liu G et al. Hoof location method of lame dairy cows based on machine vision[J]. Transactions of the Chinese Society for Agricultural Machinery, 50, 276-282(2019).
[5] Zhang X L, Liang Y. A baby-mimic insufficient-DOF quadruped crawling robot[J]. Robot, 38, 458-466(2016).
[6] Daou H E, Libourel P A, Renous S et al. Methods and experimental protocols to design a simulated bio-mimetic quadruped robot[J]. International Journal of Advanced Robotic Systems, 10, 256(2013).
[7] Kim C H, Shin H C, Lee H H. Trotting gait analysis of a lizard using motion capture[C], 1247-1251(2013).
[8] Liu B, Zhu W X, Yang J J et al. Extracting of pig gait frequency feature based on depth image and pig skeleton endpoints analysis[J]. Transactions of the Chinese Society of Agricultural Engineering, 30, 131-137(2014).
[9] Cangar Ö, Leroy T, Guarino M et al. Automatic real-time monitoring of locomotion and posture behaviour of pregnant cows prior to calving using online image analysis[J]. Computers and Electronics in Agriculture, 64, 53-60(2008).
[10] Xue F F, Wang Y M, Li Q. Recognition of cattle daily behavior based on spatial relationship of feature parts[J]. Laser & Optoelectronics Progress, 58, 2215007(2021).
[11] Cheng X Y, Zhao L Z, Hu Q et al. Real-time semantic segmentation based on dilated convolution smoothing and lightweight up-sampling[J]. Laser & Optoelectronics Progress, 57, 021017(2020).
[12] Zhang W X, Zhu Z C, Zhang Y H et al. Cell image segmentation method based on residual block and attention mechanism[J]. Acta Optica Sinica, 40, 1710001(2020).
[13] Badrinarayanan V, Kendall A, Cipolla R. SegNet: a deep convolutional encoder-decoder architecture for image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481-2495(2017).
[14] Zhao H S, Shi J P, Qi X J et al. Pyramid scene parsing network[C], 6230-6239(2017).
[15] Chen L C, Papandreou G, Kokkinos I et al. Semantic image segmentation with deep convolutional nets and fully connected CRFs[EB/OL]. https://arxiv.org/abs/1412.7062v2
[16] Chen L C, Papandreou G, Kokkinos I et al. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 40, 834-848(2018).
[17] Chen L C, Papandreou G, Schroff F et al. Rethinking atrous convolution for semantic image segmentation[EB/OL]. https://arxiv.org/abs/1706.05587
[18] Chen L C, Zhu Y, Papandreou G et al. Encoder-decoder with atrous separable convolution for semantic image segmentation[EB/OL]. http://export.arxiv.org/abs/1802.02611
[19] Howard A G, Zhu M, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861
[20] Chollet F. Xception: deep learning with depthwise separable convolutions[C], 1800-1807(2017).
[21] Tian Z, He T, Shen C H et al. Decoders matter for semantic segmentation: data-dependent decoding enables flexible feature aggregation[C], 3121-3130(2019).
[22] Sun K, Xiao B, Liu D et al. Deep high-resolution representation learning for human pose estimation[C], 5686-5696(2019).