Abstract
Animal health monitoring and population management are critical aspects of wildlife conservation and livestock management that increasingly rely on automated detection and tracking systems. While Unmanned Aerial Vehicle (UAV) based systems combined with computer vision offer promising solutions for non-invasive animal monitoring across challenging terrains, limited availability of labeled training data remains an obstacle in developing effective deep learning (DL) models for these applications. Transfer learning has emerged as a potential solution, allowing models trained on large datasets to be adapted for resource-limited scenarios such as those with limited data. However, the vast landscape of pre-trained neural network architectures makes it challenging to select optimal models, particularly for researchers new to the field. In this paper, we propose a reinforcement learning (RL)-based transfer learning framework that employs an upper confidence bound (UCB) algorithm to automatically select the most suitable pre-trained model for animal detection tasks. Our approach systematically evaluates and ranks candidate models based on their performance, streamlining the model selection process. Experimental results demonstrate that our framework achieves a higher detection rate while requiring significantly less computational time compared to traditional methods.
Abstract (translated)
动物健康监测和种群管理是野生动物保护和家畜管理中至关重要的方面，这些领域越来越依赖于自动化检测和跟踪系统。基于无人驾驶飞机（UAV）结合计算机视觉的系统为非侵入性动物监测提供了有前景的解决方案，尤其是在复杂地形条件下。然而，标记训练数据的有限可用性仍然是开发有效的深度学习（DL）模型以应对这些应用的主要障碍。迁移学习作为一种潜在的解决方案已崭露头角，它允许在大型数据集上进行训练的模型适应于资源受限的情景，例如数据量较小的情况。但是，大量的预训练神经网络架构使得选择最佳模型变得具有挑战性，特别是对于新进入该领域的研究人员而言。 本文提出了一种基于强化学习（RL）的迁移学习框架，该框架采用上置信界（UCB）算法自动选择最适合动物检测任务的预训练模型。我们的方法系统地评估和排名候选模型，并根据它们的表现对这些模型进行排序，从而简化了模型选择的过程。实验结果表明，与传统方法相比，我们提出的框架在提高检测率的同时显著减少了计算时间。
URL
https://arxiv.org/abs/2509.10995
