Abstract
Modern agricultural operations increasingly rely on integrated monitoring systems that combine multiple data sources for farm optimization. Aerial drone-based animal health monitoring serves as a key component but faces limited data availability, compounded by scene-specific issues such as small, occluded, or partially visible animals. Transfer learning approaches often fail to address this limitation due to the unavailability of large datasets that reflect specific farm conditions, including variations in animal breeds, environments, and behaviors. Therefore, there is a need for developing a problem-specific, animal-focused data augmentation strategy tailored to these unique challenges. To address this gap, we propose an object-focused data augmentation framework designed explicitly for animal health monitoring in constrained data settings. Our approach segments animals from backgrounds and augments them through transformations and diffusion-based synthesis to create realistic, diverse scenes that enhance animal detection and monitoring performance. Our initial experiments demonstrate that our augmented dataset yields superior performance compared to our baseline models on the animal detection task. By generating domain-specific data, our method empowers real-time animal health monitoring solutions even in data-scarce scenarios, bridging the gap between limited data and practical applicability.
Abstract (translated)
现代农业操作越来越依赖于结合多个数据来源的综合监测系统，以实现农场优化。基于无人机的动物健康监测是这一过程中的关键组成部分，但面临着有限的数据可用性问题，尤其是在特定场景中，如小型、被遮挡或部分可见的动物情况下尤为突出。由于缺乏能够反映具体农场条件的大规模数据集（包括不同品种、环境和行为的变化），迁移学习方法往往无法有效解决这些问题。因此，有必要开发一种专门针对这些独特挑战的问题特定、以动物为中心的数据增强策略。 为了解决这一缺口，我们提出了一种面向对象的、专为在受限数据环境中进行动物健康监测设计的数据增强框架。我们的方法通过分割动物与背景，并通过对动物应用变换和基于扩散的合成来生成现实且多样的场景，从而增强动物检测和监控性能。初步实验表明，在动物检测任务上，使用我们扩增后的数据集比基线模型表现出更优的效果。 通过生成特定领域的数据，这种方法能够在数据匮乏的情况下为实时动物健康监测解决方案提供支持，并弥合了有限数据与实际应用之间的差距。
URL
https://arxiv.org/abs/2510.08955
