Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information

Fuentes, Alvaro; Yoon, Sook; Park, Jongbin; Park, Dong Sun

doi:10.1016/j.compag.2020.105627

Cited by 77 publications

(48 citation statements)

References 46 publications

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“…For some applications of animal species recognition, professional labeling knowledge in animals may not be required since labelers only needed to distinguish animals from their respective backgrounds [ 59 ]. However, as for behavior recognition, animal scientists were generally required to assist in behavior labeling, because labels of animal behaviors should be judged accurately by professional knowledge [ 102 ]. A sole well-trained labeler was typically responsible for completion of all labeling, because labels may be influenced by judgment bias from different individuals [ 129 ].…”

Section: Preparationsmentioning

confidence: 99%

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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Section: Preparationsmentioning

confidence: 99%

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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“…Many algorithms that are based on deep learning have opened ways for dramatic research advancement in computer vision such as object detection [4], [35], semantic segmentation [36], and instance segmentation [33], [16].…”

Section: Related Workmentioning

confidence: 99%

Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method

2021

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In animal husbandry, the traceability of individual cattle, their health information, and performance records greatly depend on computer vision and image processing-based approaches. However, some of these approaches perform below expectations in obtaining real-time information about individual cattle. No doubt, inaccurate segmentation and incomplete extraction of each cattle object from an image are notable contributory factors. As accurate segmentation is a prerequisite for obtaining real-time information about individual cattle, and since the algorithm of Mask R-CNN relies on the algorithm of simultaneous localization and mapping (SLAM), for the construction of the semantic map, which sometimes exchanges image background for the foreground, there is a need to enhance the available approaches towards achieving precision animal husbandry. To achieve this, an enhanced Mask R-CNN instance segmentation method is proposed to support indistinct boundaries and irregular shapes of cattle bodies. The methods employed in the research are in multiple folds: (1) Pre-enhancement of the image using generalized color Fourier descriptors (GCFD); (2) Provision of optimal filter size that was smaller than ResNet101 (the backbone of Mask R-CNN) for the extraction of smaller and composite features;(3) Utilization of multiscale semantic features using region proposals; (4) A fully connected layer of existing Mask R-CNN integrated with a sub-network for enhanced segmentation and (5) Post-enhancement of the image using Grabcut. Experiments on the datasets of cattle images produced better results when compared to other state-of-the-art methods with 0.93 mAP.

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“…It includes automation of the house management, behavior, and welfare [11,[29][30][31][32][33][34][35][36][37][38][39][40], disease detection [28,[41][42][43][44][45][46][47], weight measurement [27,[48][49], slaughtering process [50][51], carcass quality [52][53][54][55], and egg examination [56][57][58][59][60][61][62][63][64][65]. On the other hand, computer vision also popular on other livestock monitoring, such as pig [73][74][75][76][77][78][79][80], sheep or cattle [81][82]…”

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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Deep learning-based hierarchical cattle behavior recognition with spatio-temporal information

Cited by 77 publications

References 46 publications

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

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

Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

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