Pig ID: developing a deep learning machine vision system to track pigs using individual biometrics
Lead Research Organisation:
University of the West of England
Department Name: Bristol Robotics Laboratory
Abstract
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Technical Summary
We aim to develop a new technological tool using deep learning machine vision to biometrically identify and track individual pigs. Facilitating behaviour monitoring for health and welfare, it enables a step-change in 'precision livestock farming' and has strong industry support.
Training CNNs and validating their performance requires time-consuming manual labelling of ground-truth images. Obj 1 produces this manual data increasing data volume by: a) Using a semi-supervised approach with a modified Mask-RCNN for instance segmentation, and b) Developing a new approach for identity validation: simultaneous recording with a visible light video camera paired with a modified UV-detecting camera, pigs are individually marked with invisible sun-cream which appears black under UV. Our labelled, paired videos will be a valuable public resource for other researchers.
Commercial growing pigs are not ID marked. Building on our existing machine vision technology which identifies ID from pig faces, a convolutional neural network (CNN) will be trained to recognise biometrics of individual pigs from above in a group (Obj 2; we have shown feasibility in previous work).
Next (Obj 3), we use a refined Mask-RCNN to extract individual pigs from their surroundings and implement our trained CNN enabling spatial tracking. Our innovative 'tracking by recognition' eliminates the problem of lost ID and track-swaps due to close-proximity or occlusion, by re-establishing biometric ID. We retrain the CNN to achieve long-term tracking over weeks as pigs grow and change appearance.
We will develop 'open set recognition' (Obj 4), avoiding extensive manual re-training for each new group. Previous work showed that a latent feature space can be optimised to cluster images that appear similar together (because they are from the same pig), separating them from other pigs.
Obj 5 integrates 3 and 4 refining a state-of-the-art system to enrol and track individual pigs over weeks as they grow.
Training CNNs and validating their performance requires time-consuming manual labelling of ground-truth images. Obj 1 produces this manual data increasing data volume by: a) Using a semi-supervised approach with a modified Mask-RCNN for instance segmentation, and b) Developing a new approach for identity validation: simultaneous recording with a visible light video camera paired with a modified UV-detecting camera, pigs are individually marked with invisible sun-cream which appears black under UV. Our labelled, paired videos will be a valuable public resource for other researchers.
Commercial growing pigs are not ID marked. Building on our existing machine vision technology which identifies ID from pig faces, a convolutional neural network (CNN) will be trained to recognise biometrics of individual pigs from above in a group (Obj 2; we have shown feasibility in previous work).
Next (Obj 3), we use a refined Mask-RCNN to extract individual pigs from their surroundings and implement our trained CNN enabling spatial tracking. Our innovative 'tracking by recognition' eliminates the problem of lost ID and track-swaps due to close-proximity or occlusion, by re-establishing biometric ID. We retrain the CNN to achieve long-term tracking over weeks as pigs grow and change appearance.
We will develop 'open set recognition' (Obj 4), avoiding extensive manual re-training for each new group. Previous work showed that a latent feature space can be optimised to cluster images that appear similar together (because they are from the same pig), separating them from other pigs.
Obj 5 integrates 3 and 4 refining a state-of-the-art system to enrol and track individual pigs over weeks as they grow.
