Policy-Driven Transfer Learning in Resource-Limited Animal Monitoring

• URL: http://arxiv.org/abs/2509.10995v1
• Date: Sat, 13 Sep 2025 22:26:51 GMT
• Title: Policy-Driven Transfer Learning in Resource-Limited Animal Monitoring
• Authors: Nisha Pillai, Aditi Virupakshaiah, Harrison W. Smith, Amanda J. Ashworth, Prasanna Gowda, Phillip R. Owens, Adam R. Rivers, Bindu Nanduri, Mahalingam Ramkumar,
• Abstract summary: Animal health monitoring and population management are critical aspects of wildlife conservation and livestock management.<n>Our framework achieves a higher detection rate while requiring significantly less computational time compared to traditional methods.
• Score: 0.6109833303919141
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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