综述户外牲畜视觉监测技术，揭示深度学习在动物识别中的应用趋势。
Systematic Literature Review of Vision-Based Approaches to Outdoor Livestock Monitoring with Lessons from Wildlife Studies
- 基于图像处理流程分析各阶段技术能力与挑战
- 深度学习在动物检测与多物种分类中占主导地位
- 适合关注智能养殖与野生动物监测的研究者参考
精准畜牧养殖（PLF）旨在通过先进技术提升牲畜健康与福利及养殖效益。计算机视觉结合机器学习与深度学习，为实现全天候牲畜监控提供可能，有助于早期发现健康与福利问题。然而，大量牲畜饲养于大型户外环境中，给视觉技术带来挑战。本文系统综述了户外动物监测中的计算机视觉方法与开放性问题。研究涵盖牲畜与野生动物领域，因二者在外观、行为与栖息地方面具有相似性。重点关注牛、马、鹿、山羊、绵羊、考拉、长颈鹿和大象等大型陆生哺乳动物。采用图像处理流程框架，梳理各阶段的技术能力与未解难题。结果表明，深度学习在动物检测、计数与多物种分类中呈现明显应用趋势。本文深入讨论现有视觉方法在PLF场景中的适用性，并提出未来研究的潜在方向。
原文摘要 · Abstract (English)
Precision livestock farming (PLF) aims to improve the health and welfare of livestock animals and farming outcomes through the use of advanced technologies. Computer vision, combined with recent advances in machine learning and deep learning artificial intelligence approaches, offers a possible solution to the PLF ideal of 24/7 livestock monitoring that helps facilitate early detection of animal health and welfare issues. However, a significant number of livestock species are raised in large outdoor habitats that pose technological challenges for computer vision approaches. This review provides a comprehensive overview of computer vision methods and open challenges in outdoor animal monitoring. We include research from both the livestock and wildlife fields in the review because of the similarities in appearance, behaviour, and habitat for many livestock and wildlife. We focus on large terrestrial mammals, such as cattle, horses, deer, goats, sheep, koalas, giraffes, and elephants. We use an image processing pipeline to frame our discussion and highlight the current capabilities and open technical challenges at each stage of the pipeline. The review found a clear trend towards the use of deep learning approaches for animal detection, counting, and multi-species classification. We discuss in detail the applicability of current vision-based methods to PLF contexts and promising directions for future research.
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