Pig ID: developing a deep learning machine vision system to track pigs using individual biometrics

畜牧业农民正在转向农业技术，以应对气候变化、可持续性、抗微生物耐药性和粮食安全方面的挑战，同时高效地生产出健康和福利良好的动物。机器视觉技术可以提供独特的洞察力：作为衡量个体动物健康和福利的指标，机器视觉技术可以提供独特的洞察力：24/7实时数据，记录生长/生产和行为变化。疾病、跛行或攻击性和咬尾巴等有害行为等问题可以自动检测到。在养殖场环境中持续跟踪是具有挑战性的，因为猪的形状会改变，可以挨着或叠在一起，而且最好的系统只能跟踪几分钟，除非它们被以某种方式标记(商业生长猪没有标记)。猪ID项目代表着能力上的一步变化，通过在几周甚至几个月的时间里持续监测个别无标记的猪的生长。我们之前开发了一个系统，可以从猪的面部识别(ID)个别猪。人脸识别是准确的，但定位摄像头捕捉猪的脸是困难的，而且不能捕捉整个围栏的行为。在这里，我们计划开发一个深度学习系统，使用最新的神经网络来学习识别单个猪，随着它们的生长更新并持续跟踪它们的移动。首先，在我们的人脸识别系统的基础上，我们将开发一个基于头部和身体特征的远程头顶生物识别系统。只有当对猪的ID失去信心时，才会使用逐帧跟踪作为备份，直到生物识别ID可以重新获取猪为止。这样，猪的身份和位置就会持续准确。猪的生长速度很快，所以它们的大小和外观随着时间的推移会发生很大变化。因此，接下来，我们将通过在不同周的相同猪的图像上进行测试，来确定我们训练的ID和跟踪系统对这种变化的健壮性。我们将确定它对猪外表变化的健壮程度，并定期实施生物特征ID的自动再训练，从而实现连续几周的跟踪。到目前为止，机器视觉猪跟踪只在一组或几组受过训练的猪上进行了演示。这个项目的另一个关键创新是，我们将利用我们训练有素的系统，致力于开发自动注册使用‘开放集合识别’，根据相似性对一组新的猪的图像进行聚类，以了解这些新的个体。为了实现这个项目所需的大量训练和验证数据，我们将通过结合机器视觉来半自动地在每个图像中找到猪，由人类确认并标记ID。除了亲自识别猪作为失败保险外，我们还将开发一种新的方法，使用可见颜色和紫外线(UV)摄像头并排验证ID，同时猪有独特的防晒霜标记。除了UV相机之外，它是不可见的。这些平行的UV图像将使人类能够在每一帧中手动确定猪的身份，以对照防晒霜不可见的彩色图像中的生物识别结果。一旦生物识别系统完成，它也将用于紫外线图像。它应该很容易学会识别这些标记的猪，提供进一步的验证数据。这个项目建立在我们经过验证的Face ID技术的基础上，我们正在继续我们之前的成功合作，将动物行为和福利以及农业技术方面的专业知识结合在一起，特别是在将机器视觉/学习中的尖端机器学习技术应用于真实世界的问题方面。该项目得到了相当大的商业支持，动物保健公司Zoetis联合资助了一项产业合作伙伴奖。农业科技公司Innoent Technology Ltd、生猪养殖和猪肉加工公司Karro以及养殖公司PIC准备参与下一阶段的商业化。

Livestock farmers are turning to agri-technology to respond to the challenges of climate change, sustainability, anti-microbial resistance and food security while efficiently producing animals with good health and welfare. Machine vision technology, where software uses 'deep learning' neural networks to automatically process video images, could provide unique insights: 24/7 live data on growth/production and behavioural change as a measure of individual animal health and welfare. Problems such as disease, lameness or harmful behaviours such as aggression and tail-biting could be automatically detected. Continuous tracking in a farm environment is challenging as pigs change shape and can lie next to or on top of each other, and the best available systems can only track pigs for minutes unless they are marked somehow (which commercial growing pigs are not). The Pig ID project represents a step-change in capability by continuously monitoring individual unmarked pigs over weeks and months as they grow.We previously developed a system which can identify (ID) individual pigs from their faces. Face ID is accurate but positioning cameras to capture pig faces is hard and does not capture whole-pen behaviour. Here we plan to develop a deep learning system using the latest neural networks to learn to recognise individual pigs, updating as they grow and continuously track their movements.First, building on our face recognition system, we will develop a remote overhead biometric ID system based on head and body features. Only when confidence in the ID of a pig is lost will frame-to-frame tracking be used as a backup until biometric ID can re-acquire the pig. In this way, continuous accurate pig identity and location will be established.Pigs grow rapidly, so their size and appearance change a lot over time. So next, we will determine how robust our trained ID and tracking system is to this change by testing it on images of the same pigs from different weeks. We will establish how robust it is to changing pig appearance and implement automatic retraining of biometric ID at regular intervals, enabling continued tracking over weeks.To date, machine vision pig tracking has only been demonstrated with one or a few groups of pigs that it has been trained on. Another crucial innovation of this project is that we will take our trained system and work to develop automated enrolment using 'open set recognition', clustering the images of a new group of pigs by similarity to learn about those new individuals.To achieve a large volume of training and validation data needed for this project, we will semi-automate by combining machine vision to find pigs in each image, with humans to confirm and label them with ID. Alongside in-person identification of pigs as a failsafe, we will develop a novel way of validating ID using visible colour and ultraviolet (UV) cameras side by side, while pigs have distinctive sun-cream markings, invisible except to the UV camera. These parallel UV images will enable human manual ground-truthing of pig ID in every frame, to check against biometric ID results from colour images where the sun-cream is invisible. Once the biometric ID system is complete, it will also be used with the UV images. It should easily learn to recognise these marked pigs providing further validation data.This project builds on our proven Face ID technology and we are continuing our previous successful collaboration, bringing together expertise in animal behaviour and welfare, and in agri-technology, particularly in applying cutting-edge machine learning techniques in machine vision/learning to real-word problems. The project has considerable commercial support, with co-funding from animal health company Zoetis under an Industrial Partnership Award. Agri Tech company Innovent Technology Ltd, pig farming and pork processing company Karro, and breeding company PIC are ready to be involved in the next stage of commercialisation.