Comparative study on poultry target tracking algorithms based on a deep regression network

Fang, Cheng; Huang, Junduan; Cuan, Kaixuan; Zhuang, Xiaolin; Zhang, Tiemin

doi:10.1016/j.biosystemseng.2019.12.002

Cited by 60 publications

(34 citation statements)

References 19 publications

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“…The main purpose of this process is to enhance the image quality for the segmentation step. Many image pre-processing methods have been used in researches including dilation and erosion [37], Otsu's method [37,41,44], image thresholding [59,62,57], gaussian filter [56][57]87], and binarization [41,62].…”

Section: Data Processing and Analysis Methodsmentioning

confidence: 99%

“…Computer vision has been widely used in various processes of different poultry production systems. It includes automation of the house management, behavior, and welfare [11,[29][30][31][32][33][34][35][36][37][38][39][40], disease detection [28,[41][42][43][44][45][46][47], weight measurement [27,[48][49], slaughtering process [50][51], carcass quality [52][53][54][55], and egg examination [56][57][58][59][60][61][62][63][64][65]. On the other hand, computer vision also popular on other livestock monitoring, such as pig [73][74][75][76][77][78][79][80], sheep or cattle …”

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

“…There are various types of camera that have been used in poultry farm regardless of its purposes. The particular type of imaging methods massively used in poultry farm, including visible light camera [11,[27][28][29][34][35][36][37][38][39]41,44,51], infrared [34,36], thermographic [36], depth [41,48,59], and hyperspectral camera [61][62]. While an ordinary visible-light camera captures light across three wavelength bands in the visible spectrum, including red, green, and blue (RGB), infrared, thermographic, depth and hyperspectral imaging encompasses a wide variety of spectrums that go beyond RGB.…”

Section: A Camera and Lensmentioning

confidence: 99%

“…Several studies have revealed that this type of camera is capable of capturing images for various purposes. For example, it is operated to detect the sick broilers [28,41], monitoring broiler behavior and movement [11,30,[34][35][36][37][38][39]41,44], and predict the live weight of broiler [27]. However, few major drawbacks of this camera are the lenses compatibility and resolution.…”

Section: A Camera and Lensmentioning

confidence: 99%

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A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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Section: Data Processing and Analysis Methodsmentioning

confidence: 99%

Section: Overview Of Computer Vision In Poultry Farmmentioning

confidence: 99%

Section: A Camera and Lensmentioning

confidence: 99%

Section: A Camera and Lensmentioning

confidence: 99%

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A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

et al. 2021

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“…They also combined these two metrics with device price and judged which models were more computationally and economically efficient. Fang et al [ 75 ] proposed a mixed tracking metric by combining overlapping area, failed detection, and object size.…”

Section: Strategies For Algorithm Developmentmentioning

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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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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Precision Livestock Farming (PLF) Systems: Improving Sustainability and Efficiency of Animal Production

Tzanidakis

Simitzis

Panagakis

2023

International Series in Operations Research &Amp; Management Science

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Comparative study on poultry target tracking algorithms based on a deep regression network

Cited by 60 publications

References 19 publications

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

A Review on Computer Vision Technology for Monitoring Poultry Farm—Application, Hardware, and Software

Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review

Precision Livestock Farming (PLF) Systems: Improving Sustainability and Efficiency of Animal Production

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