Image-based Individual Cow Recognition using Body Patterns

Bello, Rotimi-Williams; Talib, Abdullah Zawawi; Mohamed, Ahmad Sufril Azlan; Olubummo, D A; Noah, Otobo, Firstman

doi:10.14569/ijacsa.2020.0110311

Cited by 14 publications

(13 citation statements)

References 24 publications

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“…As for some large farm animals (e.g., cattle), their ventral parts typically had larger surface area and more diverse coating patterns than their dorsal parts. Therefore, a side view was favorable for individual cattle recognition based on their coating pattern [ 48 ]. A front view was required for face recognition of farm animals [ 49 ], and rear-view images were used to evaluate the body conditions of farm animals based on their features of rear parts [ 50 ].…”

Section: Preparationsmentioning

confidence: 99%

“…However, lighting conditions in farms are complex and diverse, and color-space patterns learned from development datasets may not be matched to those in real applications, which may lead to poor generalization performance [ 115 ]. One solution was to convert RGB images (three channels) into grayscale images (one channel) [ 48 , 55 , 70 , 88 , 116 ], so that attention of models can be diverted from object colors to learning patterns of objects. Additionally, hue, saturation, and value (HSV) imaging was not as sensitive to illumination changes as RGB imaging and may be advantageous on detecting characteristics of colorful target objects [ 114 ].…”

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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Section: Preparationsmentioning

confidence: 99%

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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“…Other notable works that demonstrated the effectiveness of CNNs in extracting features for the categorization of species are [17][18][19][20]. Other tasks in which computer vision is applied are animal identification, detection of animal disease, animal counting, and animal image segmentation [6][7][8][9]21].…”

Section: Motivationmentioning

confidence: 99%

Cow Image Segmentation Using Mask R-CNN Integrated with Grabcut

Bello

Mohamed

Talib

2021

Proceedings of International Conference on Emerging Technologies and Intelligent Systems

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Different state-of-the-art object detection methods have been applied in agriculture for precision livestock farming. However, the quality and accuracy of livestock such as cows being detected in an image during computer vision tasks depend on the segmentation and extraction techniques used. Mask R-CNN, an instance segmentation method popular for its class and mask regression has been widely applied for cow image segmentation tasks. However, its algorithm relies on simultaneous localization and mapping algorithms, thereby discrediting its ability to completely segment an image foreground from the image background. In this paper, a Mask R-CNN segmentation method integrated with Grabcut is proposed. The method is for the detection and complete extraction of an image foreground from the image background. We performed an experiment using edge detection to compare the segmentation that is region-based with the boundary estimation. After comparing the segmentation, we have concluded that the Grabcut integrated with the Mask R-CNN is based on the computation of the edge detection approach employed by our proposed method.

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“…Current progress recorded in deep learning enriches us with a direction for overcoming this challenge. The potency of deep neural networks in feature learning [13], [14] has enabled noteworthy progress in the field of computer vision, object detection, and segmentation. In the aspect of object detection, the Faster RCNN method [15] from which the Mask R-CNN [16] method was coined has greatly contributed to the robust detection of objects [17].…”

Section: Introductionmentioning

confidence: 99%

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

2021

Self Cite

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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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Image-based Individual Cow Recognition using Body Patterns

Cited by 14 publications

References 24 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

Cow Image Segmentation Using Mask R-CNN Integrated with Grabcut

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

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