Abstract
The goal of this study was to develop an automated monitoring system for the detection of pigs’ bodies, heads and tails. The aim in the first part of the study was to recognize individual pigs (in lying and standing positions) in groups and their body parts (head/ears, and tail) by using machine learning algorithms (feature pyramid network). In the second part of the study, the goal was to improve the detection of tail posture (tail straight and curled) during activity (standing/moving around) by the use of neural network analysis (YOLOv4). Our dataset (n = 583 images, 7579 pig posture) was annotated in Labelbox from 2D video recordings of groups (n = 12–15) of weaned pigs. The model recognized each individual pig’s body with a precision of 96% related to threshold intersection over union (IoU), whilst the precision for tails was 77% and for heads this was 66%, thereby already achieving human-level precision. The precision of pig detection in groups was the highest, while head and tail detection precision were lower. As the first study was relatively time-consuming, in the second part of the study, we performed a YOLOv4 neural network analysis using 30 annotated images of our dataset for detecting straight and curled tails. With this model, we were able to recognize tail postures with a high level of precision (90%).
Highlights
To monitor animal behaviour, classical approaches are used which involve real-time manual observation or the manual analysis of recorded animal behaviours
While studies have previously documented that detection precision with the R-fully convolutional networks (FCNs) ResNet101 DL-network for both standing/lying posture was of 93%, our study revealed that with the feature pyramid network (FPN) architecture, we can achieve almost human-level precision
With the use of machine learning algorithms for object detection based on feature pyramid network (FPN) architecture, the precision of individual pig detection in groups was almost the same as human-level precision
Summary
Classical approaches are used which involve real-time manual observation or the manual analysis of recorded animal behaviours. These methods are labour-intensive and a video from one experiment may take several months to analyse. Automatic monitoring is desirable and urgently needed. As is the case with image analysis techniques, these methods suffer the same problem: visual cues are unreliable and similar objects might be difficult to differentiate. With the blooming of machine learning/deep learning (ML/DL) research in images in recent years, significant improvements have been made in animal shape detection [4,5] and behavioural sequence detection [6]. Individual pigs can be identified on the basis of their inherent dimensions and colour [7]
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