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

Pig ID：开发深度学习机器视觉系统，利用个体生物识别技术跟踪猪

• 批准号: BB/X001830/1
• 负责人: Rick D'Eath
• 金额: $ 24.99万
• 依托单位: Scotland's Rural College
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FX001830%2F1
• 关键词: Pig; ID; developing; deep; learning

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.

畜牧业者正在转向农业技术，以应对气候变化、可持续性、抗微生物耐药性和粮食安全的挑战，同时有效地生产出健康和福利良好的动物。机器视觉技术，软件使用“深度学习”神经网络自动处理视频图像，可以提供独特的见解：24/7的生长/生产和行为变化实时数据，作为衡量个体动物健康和福利的指标。疾病、跛行或攻击和咬尾巴等有害行为等问题都可以自动检测出来。在农场环境中连续跟踪是具有挑战性的，因为猪会改变形状，可以躺在一起或在一起，最好的系统只能跟踪猪几分钟，除非它们以某种方式标记（商业生长猪没有）。Pig ID项目代表了能力的一个步骤变化，通过连续监测个体无标记的猪在几周和几个月内，因为他们的成长。我们以前开发了一个系统，可以识别（ID）个别猪从他们的脸。Face ID是准确的，但定位摄像头来捕捉猪的脸是困难的，并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个体猪，随着它们的成长而更新，并不断跟踪它们的运动。首先，我们将在我们的面部识别系统的基础上，开发一个基于头部和身体特征的远程头顶生物识别系统。只有当猪的ID失去信心时，帧到帧的跟踪才被用作备份，直到生物识别ID可以重新获取猪。这样就能建立起持续准确的猪的身份和位置。猪生长迅速，因此随着时间的推移，它们的大小和外观会发生很大变化。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，来确定我们经过训练的ID和跟踪系统对这种变化的鲁棒性。我们将确定它对改变猪的外观的鲁棒性，并定期实施生物识别ID的自动再培训，从而实现数周的持续跟踪。迄今为止，机器视觉猪跟踪只在一组或几组猪上进行了训练。该项目的另一个关键创新是，我们将采用我们的训练系统，并使用“开放集”开发自动注册为了获得该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化地在每张图像中找到猪，并由人类进行确认并用ID标记它们。除了亲自识别猪作为故障保险措施外，我们将开发一种新的方法，通过并排使用可见颜色和紫外线（UV）相机来验证ID，而猪有独特的防晒霜标记，除了紫外线相机外，看不到。这些平行的紫外线图像将使人类能够在每一帧中手动地面验证猪的身份，以检查彩色图像的生物识别结果，其中防晒霜是不可见的。一旦生物识别ID系统完成，它也将与紫外线图像一起使用。该项目建立在我们成熟的Face ID技术基础上，我们将继续之前的成功合作，汇集动物行为和福利以及农业技术方面的专业知识，特别是将机器视觉/学习中的尖端机器学习技术应用于现实问题。该项目得到了相当大的商业支持，动物保健公司硕腾公司在工业合作奖下共同资助。农业技术公司Innovent Technology Ltd、养猪和猪肉加工公司Karro以及育种公司PIC已准备好参与下一阶段的商业化。