Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN

Zhu, Xunmu; Chen, Changxin; Zheng, Bin; Yang, Xin‐She; Gan, Haiming; Zheng, Changqing; Yang, Aqing; Mao, Liang; Xue, Yong

doi:10.1016/j.biosystemseng.2019.11.013

Cited by 34 publications

(14 citation statements)

References 23 publications

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“…To tackle these challenges, convolutional neural networks (CNNs) have been proposed to accurately detect pigs 21 – 25 . The dynamic filter selection in CNNs enables these methods to achieve invariance to different farm conditions 26 , 27 . A limitation associated with existing deep learning based methods includes the requirement for pigs to be individually marked, adding unscalable labour burden to mark the pigs.…”

Section: Introductionmentioning

confidence: 99%

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

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Changes in pig behaviours are a useful aid in detecting early signs of compromised health and welfare. In commercial settings, automatic detection of pig behaviours through visual imaging remains a challenge due to farm demanding conditions, e.g., occlusion of one pig from another. Here, two deep learning-based detector methods were developed to identify pig postures and drinking behaviours of group-housed pigs. We first tested the system ability to detect changes in these measures at group-level during routine management. We then demonstrated the ability of our automated methods to identify behaviours of individual animals with a mean average precision of , under a variety of settings. When the pig feeding regime was disrupted, we automatically detected the expected deviations from the daily feeding routine in standing, lateral lying and drinking behaviours. These experiments demonstrate that the method is capable of robustly and accurately monitoring individual pig behaviours under commercial conditions, without the need for additional sensors or individual pig identification, hence providing a scalable technology to improve the health and well-being of farm animals. The method has the potential to transform how livestock are monitored and address issues in livestock farming, such as targeted treatment of individuals with medication.

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Section: Introductionmentioning

confidence: 99%

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Alameer

Kyriazakis

Bacardit

2020

Sci Rep

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“…Including depth information may further improve the detection performance. Zhu et al [ 52 ] paired RGB and depth images and input them simultaneously into a detector. The combination of these two types of images outperformed the use of RGB imaging or depth imaging singly.…”

Section: Preparationsmentioning

confidence: 99%

“…Alvarez et al [ 108 ] complemented confidence of inference of minority class via increasing class weights for them. Zhu et al [ 52 ] set a larger frame interval for sampling images containing high-frequency behaviors. Another alternative method was to synthesize and augment images for minority classes during training [ 57 ].…”

Section: Preparationsmentioning

confidence: 99%

“…As for enhancement in spatial domain, manipulations in histograms are commonly used to improve brightness and contrast of an image. Examples include histogram equalization [ 73 , 121 ], adaptive histogram equalization [ 52 , 67 ], and contrast limited adaptive histogram equalization [ 70 , 89 , 122 ]. Other intensity transformation methods (e.g., adaptive segmented stretching, adaptive nonlinear S-curve transform, and weighted fusion) were also used to enhance the intensities or intensity changes in images [ 53 ].…”

Section: Preparationsmentioning

confidence: 99%

“…In addition, some restoration models can be applied to remove the noises processed during data acquisition, encoding, and transmission and to improve image quality [ 126 ]. Median filtering that averages intensities based on neighboring pixels was used in general [ 52 , 110 , 122 ], and other models that included two-dimensional gamma function-based image adaptive correction algorithm and several layers of neural networks were also available for the denoising [ 63 , 126 , 127 ]. Some images contain natural noises that are hard to be denoised.…”

Section: Preparationsmentioning

confidence: 99%

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Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Huang

Chen

et al. 2021

Sensors

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Convolutional neural network (CNN)-based computer vision systems have been increasingly applied in animal farming to improve animal management, but current knowledge, practices, limitations, and solutions of the applications remain to be expanded and explored. The objective of this study is to systematically review applications of CNN-based computer vision systems on animal farming in terms of the five deep learning computer vision tasks: image classification, object detection, semantic/instance segmentation, pose estimation, and tracking. Cattle, sheep/goats, pigs, and poultry were the major farm animal species of concern. In this research, preparations for system development, including camera settings, inclusion of variations for data recordings, choices of graphics processing units, image preprocessing, and data labeling were summarized. CNN architectures were reviewed based on the computer vision tasks in animal farming. Strategies of algorithm development included distribution of development data, data augmentation, hyperparameter tuning, and selection of evaluation metrics. Judgment of model performance and performance based on architectures were discussed. Besides practices in optimizing CNN-based computer vision systems, system applications were also organized based on year, country, animal species, and purposes. Finally, recommendations on future research were provided to develop and improve CNN-based computer vision systems for improved welfare, environment, engineering, genetics, and management of farm animals.

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Maneuvers Under Estimation of Human Postures for Autonomous Navigation of Robot KUKA YouBot

Gordón

Barahona

Cumbajín

et al. 2020

Advances in Intelligent Systems and Computing

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Automatic recognition of lactating sow postures by refined two-stream RGB-D faster R-CNN

Cited by 34 publications

References 23 publications

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Automated recognition of postures and drinking behaviour for the detection of compromised health in pigs

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Maneuvers Under Estimation of Human Postures for Autonomous Navigation of Robot KUKA YouBot

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