A detection method for dead caged hens based on improved YOLOv7

Yang, Jikang; Zhang, Tiemin; Fang, Cheng; Zheng, Haikun; Ma, Chuang; Wu, Zhenlong

doi:10.1016/j.compag.2024.109388

Computers and Electronics in Agriculture

2024

DOI: 10.1016/j.compag.2024.109388

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A detection method for dead caged hens based on improved YOLOv7

Jikang Yang,

Tiemin Zhang,

Cheng Fang

et al.

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Cited by 3 publications

References 31 publications

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Research on machine vision online monitoring system for egg production and quality in cage environment

Wu,

Zhang,

Fang

2025

Poultry Science

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Research on machine vision online monitoring system for egg production and quality in cage environment

Wu,

Zhang,

Fang

2025

Poultry Science

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Dead Broiler Detection and Segmentation Using Transformer-Based Dual Stream Network

Ham,

2024

Agriculture

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Improving productivity in industrial farming is crucial for precision agriculture, particularly in the broiler breeding sector, where swift identification of dead broilers is vital for preventing disease outbreaks and minimizing financial losses. Traditionally, the detection process relies on manual identification by farmers, which is both labor-intensive and inefficient. Recent advances in computer vision and deep learning have resulted in promising automatic dead broiler detection systems. In this study, we present an automatic detection and segmentation system for dead broilers that uses transformer-based dual-stream networks. The proposed dual-stream method comprises two streams that reflect the segmentation and detection networks. In our approach, the detection network supplies location-based features of dead broilers to the segmentation network, aiding in the prevention of live broiler mis-segmentation. This integration allows for more accurate identification and segmentation of dead broilers within the farm environment. Additionally, we utilized the self-attention mechanism of the transformer to uncover high-level relationships among the features, thereby enhancing the overall accuracy and robustness. Experiments indicated that the proposed approach achieved an average IoU of 88% on the test set, indicating its strong detection capabilities and precise segmentation of dead broilers.

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The Posture Detection Method of Caged Chickens Based on Computer Vision

Fang,

Zhuang,

Zheng

et al. 2024

Animals

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At present, raising caged chickens is a common farming method in China. However, monitoring the status of caged chickens is still done by human labor, which is time-consuming and laborious. This paper proposed a posture detection method for caged chickens based on computer vision, which can automatically identify the standing and lying posture of chickens in a cage. For this aim, an image correction method was used to rotate the image and make the feeding trough horizontal in the image. The variance method and the speeded-up robust features method were proposed to identify the feeding trough and indirectly obtain the key area through the feeding trough position. In this paper, a depth camera was used to generate three-dimensional information so that it could extract the chickens from the image of the key area. After some constraint conditions, the chickens’ postures were screened. The experimental results show that the algorithm can achieve 97.80% precision and 80.18% recall (IoU > 0.5) for white chickens and can achieve 79.52% precision and 81.07% recall (IoU > 0.5) for jute chickens (yellow and black feathers). It runs at ten frames per second on an i5-8500 CPU. Overall, the results indicated that this study provides a non-invasive method for the analysis of posture in caged chickens, which may be helpful for future research on poultry.

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A detection method for dead caged hens based on improved YOLOv7

Cited by 3 publications

References 31 publications

Research on machine vision online monitoring system for egg production and quality in cage environment

Research on machine vision online monitoring system for egg production and quality in cage environment

Dead Broiler Detection and Segmentation Using Transformer-Based Dual Stream Network

The Posture Detection Method of Caged Chickens Based on Computer Vision

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