A Robust Occlusion-Aware Deep Learning Architecture for Thermal Aerial Person Classification in Search-and-Rescue Missions

Abstract

Uncrewed aerial vehicles equipped with long-wave infrared cameras are a promising tool for locating missing persons, yet their performance collapses when more than 70% of a human body is obscured by vegetation. We address this limitation by reformulating the task as a thermal classification problem rather than a detection problem, allowing the system to focus on thermal appearance cues that remain visible under heavy occlusion. Recognition robustness is further affected by high-frequency infrared sensor noise, which increases with flight altitude. Using five-fold cross-validation at flight altitudes of 30 m, 50 m, and 70 m, we show that the proposed classification pipeline achieves an accuracy of 99.07% and maintains strong recall under heavy occlusion while operating at a low computational cost. Analysis of altitude-specific models demonstrates that the Gaussian-enhanced pipeline provides a statistically significant improvement in robustness, with the largest gains observed at higher flight altitudes due to more effective suppression of altitude-dependent sensor noise. These findings establish an operational robustness benchmark for occlusion-aware thermal person classification and provide a reproducible foundation for improving the reliability of uncrewed aerial vehicle search-and-rescue systems.