YOLOv7 Applied to Livestock Image Detection and Segmentation Tasks in Cattle Grazing Behavior, Monitor and Intrusions

Moradeyo, O. M.; Olaniyan, Azeez; Ojoawo, Adesola Ojo; Olawale, Jegede; Bello, R. W.

doi:10.4314/jasem.v27i5.10

Cited by 1 publication

(1 citation statement)

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“…The use of various intelligent gadgets in contemporary livestock farming together with computer vision technology in livestock production has become more commonplace in recent years and has become a hotspot for livestock farming research [10][11][12]. The utilization of these intelligent devices has markedly enhanced livestock-raising accuracy and efficiency, reduced dependence on labor and resources, lowered livestock-raising costs, and fostered sustainable growth in the livestock industry.…”

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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Section: Introductionmentioning

confidence: 99%