Detection and Breed Classification of Cattle Using YOLO v4 Algorithm

Yılmaz, Aydın; Uzun, Gizem Nur; Gürbüz, Mustafa Zahid; Kivrak, Oğuzhan

doi:10.1109/inista52262.2021.9548440

Cited by 14 publications

(6 citation statements)

References 5 publications

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“…Tassinari et al [25] used deep learning techniques to automatically identify a single cow in a video recorded in a livestock barn. Yılmaz et al [26] studied the performance of cattle detection and breed classification based on specific regions of different cattle breeds, using the YOLO algorithm to detect and classify livestock in the dataset. In addition, Kang et al [27] proposed a cow lameness detection method consisting of video downscaling and deep learning algorithms.…”

Section: B Yolo Identification Methodsmentioning

confidence: 99%

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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Cattle's individual identification plays a crucial role in effectively managing large farms. To enhance agricultural efficiency, promote the digital transformation of animal husbandry, and improve animal welfare, it is essential to employ advanced identification technologies capable of real-time monitoring of cattle individual. This paper introduces a novel network called Open Pose Mask R-CNN (OP-Mask R-CNN) for individual cattle identification, which combines Open Pose with the Mask R-CNN network. Three key strategies are presented to improve the identification of individual cattle. First, optimize the number of convolutional layers in the Mask R-CNN backbone network, i.e., ResNet101. Second, introduce an Open Pose-based bovine skeleton feature extraction method. Finally, construct a fusion mechanism that combines the attention module, the convolutional block attention module (CBAM), the open pose module, and the ResNet101. Experimental results demonstrate that our proposed method achieves a 5.6% increase in recognition accuracy and improves recognition speed compared to the original Mask R-CNN model. This work strikes a balance between accuracy and complexity, facilitating the development of a lightweight bovine individual recognition technique.

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Section: B Yolo Identification Methodsmentioning

confidence: 99%

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Wang,

Zhang,

Gao

et al. 2023

IEEE Access

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“…In these aforementioned works, the researchers successfully used deep learning techniques to classify or recognise cattle, however, the references to accuracy parameters and experimental conditions in the research content of literatures [11] [13] [15] were not sufficient. The studies in Literature [12] [14] [16] used the mainstream YOLO family of algorithms for cattle recognition and detection, however, the comparison with mainstream target classification algorithms such as CNN was not adequate and suffered from the limitation that the model comparison was not comprehensive enough. This leads to a situation where their accuracy is not very high and ignores the possibility of applying some new techniques to identify and classify cattle breeds.…”

Section: S Chen Et Almentioning

confidence: 99%

“…In this study, SVM machine learning algorithm is used to identify the images of facial morphology and various body sizes of cattle. Yılmaz A et al [12] mentioned that breed classification is the use of computer vision techniques to classify such detected animals. Its research aims to evaluate the performance of cattle detection and breed classification based on specific regions of the cattle that vary by breed.You Only Look Once (YOLO) This algorithm is used for cattle detection and breed classification on the dataset.…”

Section: Introductionmentioning

confidence: 99%

RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle

Li,

Fang,

Zhao

2024

Preprint

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In the farming industry in order to cope with the problems such as the complex environment of the pasture and dense targets, etc., which lead to increased difficulty in recognition, so as to quickly classify and automatically identify cattle breeds and improve the accuracy. In this paper, an RTDETR-Refa (Rep- Conv Efficient Faster Attention) model is proposed for classification and identification of cattle breeds. Firstly, some novel improvements have been made to ResNet18. Replacing the 1×1 convolution in Faster-Block with the reparameterised RepConv, and adding the Efficient Multiscale Attention Module (EMA) in front of the global average pooling layer in Faster-Block to enhance the transformation and classification of features, Finally, the 4-layer BasicBlock after the 3 convolutional layers in the resnet18-backbone is replaced by the improved Faster-Block.Finally, the results of the training tests of the RTDETR-Refa model are compared with other classical models: the YOLO family of models: the YOLOv5m, YOLOv6, YOLOv8m, YOLOv9 and the state-of-the-art (SOTA) CNN backbone networks EfficientViT, FasterNet, UniRepLKNet, TransNeXt verifying their superiority. The average accuracy of the RTDETR-Refa model on the cattle classification training set is 91.6%, which is 0.8% higher than that of ResNet18 and 0.9-5.2% higher than that of other classical models. The experimental results show that the RTDETR-Refa model proposed in this paper is capable of identifying and classifying cattle of different breeds while ensuring similar detection speeds, demonstrating the feasibility of convolutional neural networks in breed identification and classification.

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“…This library provides an API to recognize different objects using pre-trained models. The You Only Look Once (YOLO) is one of the most used object detection models (Yilmaz et al, 2021). YOLO v3 was used as the person detection model in this study.…”

Section: Feature Extractionmentioning

confidence: 99%

Automatic Classification of Learning Material Styles

Aquino,

Souza,

Barrére

2023

RBIE

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Although video lessons are often used in diverse areas, the lack of a common approach to defining and classifying their styles results in using many different models for these purposes. There is a need to build a framework through which these styles can be defined and classified. Much has been done to investigate the effects of these styles on student engagement and learning outcomes. These studies suggest that video lesson styles affect academic performance and that students learn better through a certain video lesson style. Based on this, we propose a unified model for classifying video lesson styles based on the nomenclatures and definitions used in the literature. Furthermore, we present an approach for automatically classifying four popular video lesson styles. The automatic classification is useful for recommendation systems to suggest materials more consistent with student preferences and their intended learning outcomes.

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Detection and Breed Classification of Cattle Using YOLO v4 Algorithm

Cited by 14 publications

References 5 publications

Open Pose Mask R-CNN Network for Individual Cattle Recognition

Open Pose Mask R-CNN Network for Individual Cattle Recognition

RTDETR-Refa: a real-time detection method for multi-breed classiffcation of cattle

Automatic Classification of Learning Material Styles

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