Research on Chengdu Ma Goat Recognition Based on Computer Vison

Pu, Jingyu; Yu, Chengjun; Xiao-yan, Chen; Yang, Xin; Li, Jun

doi:10.3390/ani12141746

Cited by 6 publications

(4 citation statements)

References 30 publications

(44 reference statements)

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“…We constructed a video dataset of Chengdu Ma goats that can be used for object tracking and integrated the improved object detection algorithm and the object tracking algorithm into a framework that can effectively solve the counting problem of captive goats without additional infrastructure assistance, which has good applicability in a practical application scenario. We believe that in the near future, combined with our work and that of other authors [ 32 ], intelligent breeding and management of Chengdu Ma goats will become possible.…”

Section: Discussionmentioning

confidence: 65%

An Integrated Goat Head Detection and Automatic Counting Method Based on Deep Learning

Liu

Chen

et al. 2022

Animals

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Goat farming is one of the pillar industries for sustainable development of national economies in some countries and plays an active role in social and economic development. In order to realize the precision and intelligence of goat breeding, this paper describes an integrated goat detection and counting method based on deep learning. First, we constructed a new dataset of video images of goats for the object tracking task. Then, we took YOLOv5 as the baseline of the object detector and improved it using a series of advanced methods, including: using RandAugment to explore suitable data augmentation strategies in a real goat barn environment, using AF-FPN to improve the network’s ability to represent multi-scale objects, and using the Dynamic Head framework to unify the attention mechanism with the detector’s heads to improve its performance. The improved detector achieved 92.19% mAP, a significant improvement compared to the 84.26% mAP of the original YOLOv5. In addition, we also input the information obtained by the detector into DeepSORT for goat tracking and counting. The average overlap rate of our proposed method is 89.69%, which is significantly higher than the 82.78% of the original combination of YOLOv5 and DeepSORT. In order to avoid double counting as much as possible, goats were counted using the single-line counting based on the results of goat head tracking, which can support practical applications.

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Section: Discussionmentioning

confidence: 65%

An Integrated Goat Head Detection and Automatic Counting Method Based on Deep Learning

Liu

Chen

et al. 2022

Animals

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“…Recently four architectures for training the models: VGG16, ResNet152V2, InceptionV3, and DenseNet201 have been used to classify segments of a sheep using an image dataset of 512 images from 32 sheep for superpixels classification and segmentation of sheep in view of animal tracking and weight prediction [ 25 ]. A self-supervised deep learning module has been implemented to accurately recognize Chengdu ma goats [ 26 ]. Deep learning based identification model considers an image as input and processes it through different layers, the convolutional layer, the pooling layer, the ReLU activation layer and the fully-connected layer.…”

Section: Methodsmentioning

confidence: 99%

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

Laishram¹,

Mandal²,

Haldar³

et al. 2023

Anim Biosci

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Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system.Methods: Forty nine healthy, disease free, 3 months ± 6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9 and 12 months of age using a specialized camera made for human iris scanner. iGoat software was used for matching the same individual goats at 3, 6, 9 and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results:The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9 and 12 months of ages were recorded as 81.63%, 90.24%, 44.44% and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17% and 87.76% for identification of goat at 3, 6, 9 and 12 months of ages, respectively after training the model.

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“…This technique successfully identifies Chengdu ma goats in actual indoor cowshed breeding settings, laying the groundwork for precision livestock feeding according to age and gender. However, its limitation lies in its applicability being restricted to indoor breeding scenarios and its focus on a single livestock species [21]. Kurniadi and Setianingsih et al combined YOLOv5 with UAV imagery and videography to achieve recognition and localization of free-grazing dairy cattle.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Livestock Detection: An Efficient Model Based on YOLOv8

Fang,

Li,

Yang

et al. 2024

Applied Sciences

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Maintaining a harmonious balance between grassland ecology and local economic development necessitates effective management of livestock resources. Traditional approaches have proven inefficient, highlighting an urgent need for intelligent solutions. Accurate identification of livestock targets is pivotal for precise livestock farming management. However, the You Only Look Once version 8 (YOLOv8) model exhibits limitations in accuracy when confronted with complex backgrounds and densely clustered targets. To address these challenges, this study proposes an optimized CCS-YOLOv8 (Comprehensive Contextual Sensing YOLOv8) model. First, we curated a comprehensive livestock detection dataset encompassing the Qinghai region. Second, the YOLOv8n model underwent three key enhancements: (1) incorporating a Convolutional Block Attention Module (CBAM) to accentuate salient image information, thereby boosting feature representational power; (2) integrating a Content-Aware ReAssembly of FEatures (CARAFE) operator to mitigate irrelevant interference, improving the integrity and accuracy of feature extraction; and (3) introducing a dedicated small object detection layer to capture finer livestock details, enhancing the recognition of smaller targets. Experimental results on our dataset demonstrate the CCS-YOLOv8 model’s superior performance, achieving 84.1% precision, 82.2% recall, 84.4% mAP@0.5, 60.3% mAP@0.75, 53.6% mAP@0.5:0.95, and 83.1% F1-score. These metrics reflect substantial improvements of 1.1%, 7.9%, 5.8%, 6.6%, 4.8%, and 4.7%, respectively, over the baseline model. Compared to mainstream object detection models, CCS-YOLOv8 strikes an optimal balance between accuracy and real-time processing capability. Its robustness is further validated on the VisDrone2019 dataset. The CCS-YOLOv8 model enables rapid and accurate identification of livestock age groups and species, effectively overcoming the challenges posed by complex grassland backgrounds and densely clustered targets. It offers a novel strategy for precise livestock population management and overgrazing prevention, aligning seamlessly with the demands of modern precision livestock farming. Moreover, it promotes local environmental conservation and fosters sustainable development within the livestock industry.

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Research on Chengdu Ma Goat Recognition Based on Computer Vison

Cited by 6 publications

References 30 publications

An Integrated Goat Head Detection and Automatic Counting Method Based on Deep Learning

An Integrated Goat Head Detection and Automatic Counting Method Based on Deep Learning

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

Enhancing Livestock Detection: An Efficient Model Based on YOLOv8

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