Segmentation of sows in farrowing pens

Tu, G.; Karstoft, Henrik; Pedersen, Lene Juul; Jørgensen, Erik

doi:10.1049/iet-ipr.2012.0734

Cited by 14 publications

(10 citation statements)

References 29 publications

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“…This method demonstrated effective segmentation of a sow’s body, even when she was partially occluded by the crate elements. Tu et al addressed the issues of dynamic background objects, light changes, and motionless foreground objects in their study of sows in free-farrowing pens using a Gaussian mixture model for background subtraction and dyadic wavelength transformation, and then performed tracking using the centre of mass [47].…”

Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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Large-scale phenotyping of animal behaviour traits is time consuming and has led to increased demand for technologies that can automate these procedures. Automated tracking of animals has been successful in controlled laboratory settings, but recording from animals in large groups in highly variable farm settings presents challenges. The aim of this review is to provide a systematic overview of the advances that have occurred in automated, high throughput image detection of farm animal behavioural traits with welfare and production implications. Peer-reviewed publications written in English were reviewed systematically following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. After identification, screening, and assessment for eligibility, 108 publications met these specifications and were included for qualitative synthesis. Data collected from the papers included camera specifications, housing conditions, group size, algorithm details, procedures, and results. Most studies utilized standard digital colour video cameras for data collection, with increasing use of 3D cameras in papers published after 2013. Papers including pigs (across production stages) were the most common (n = 63). The most common behaviours recorded included activity level, area occupancy, aggression, gait scores, resource use, and posture. Our review revealed many overlaps in methods applied to analysing behaviour, and most studies started from scratch instead of building upon previous work. Training and validation sample sizes were generally small (mean±s.d. groups = 3.8±5.8) and in data collection and testing took place in relatively controlled environments. To advance our ability to automatically phenotype behaviour, future research should build upon existing knowledge and validate technology under commercial settings and publications should explicitly describe recording conditions in detail to allow studies to be reproduced.

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Section: Resultsmentioning

confidence: 99%

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

et al. 2019

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“…Today, information technology is a key tool to improve the management of cattle and dairy herds in the industry. Many technologies, like computer vision-based systems, help us to detect and track animals, and to analyze their social behavior, thus enabling us to detect changes in their usual behavior in a farm environment [1]- [3].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Segmentation of Cattle Images Using Deep Learning

Sousa

Backes

2021

Anais Do XVII Workshop De Visão Computacional (WVC 2021)

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In this work, we used the Deep Learning (DL) architecture named U-Net to segment images containing side view cattle. We evaluated the ability of the U-Net to segment images captured with different backgrounds and from the different breeds, both acquired by us and from the Internet. Since cattle images present a more constant background than other applications, we also evaluated the performance of the U-Net when we change the numbers of convolutional blocks and filters. Results show that U-Net can be used to segment cattle images using fewer blocks and filters than traditional U-Net and that the number of blocks is more important than the total number of filters used.

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“…Several studies have recently used surveillance techniques to automatically monitor livestock [ 1 , 2 , 3 , 4 ]. In this study, we focus on video-based pig monitoring applications with non-attached (i.e., non-invasive) sensors [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 ]. Furthermore, we employ a top-view depth sensor [ 17 , 18 , 19 , 20 , 21 , 22 ] due to the practical difficulties presented in commercial farms where the light is turned off at night (i.e., light fluctuations, shadowing, cluttered backgrounds, varying floor status caused by urine/manure, etc.).…”

Section: Introductionmentioning

confidence: 99%

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

Choi

Seo

et al. 2018

Sensors

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Segmenting touching-pigs in real-time is an important issue for surveillance cameras intended for the 24-h tracking of individual pigs. However, methods to do so have not yet been reported. We particularly focus on the segmentation of touching-pigs in a crowded pig room with low-contrast images obtained using a Kinect depth sensor. We reduce the execution time by combining object detection techniques based on a convolutional neural network (CNN) with image processing techniques instead of applying time-consuming operations, such as optimization-based segmentation. We first apply the fastest CNN-based object detection technique (i.e., You Only Look Once, YOLO) to solve the separation problem for touching-pigs. If the quality of the YOLO output is not satisfied, then we try to find the possible boundary line between the touching-pigs by analyzing the shape. Our experimental results show that this method is effective to separate touching-pigs in terms of both accuracy (i.e., 91.96%) and execution time (i.e., real-time execution), even with low-contrast images obtained using a Kinect depth sensor.

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Segmentation of sows in farrowing pens

Cited by 14 publications

References 29 publications

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Recording behaviour of indoor-housed farm animals automatically using machine vision technology: A systematic review

Unsupervised Segmentation of Cattle Images Using Deep Learning

A Kinect-Based Segmentation of Touching-Pigs for Real-Time Monitoring

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