Abstract
Thermal surveillance systems deployed over months must remain accurate despite environmental thermal drift induced by seasonal weather variation, ambient temperature changes, and sensor recalibration. We present results on the Long-Term Thermal Detection (LTDv2) benchmark using a YOLOv11m detector adapted for single-channel long-wave infrared (LWIR) imagery via a thermally consistent training pipeline: we disable RGB-centric color augmentations (e.g., HSV hue/saturation) that are invalid for grayscale LWIR and emphasize intensity-domain perturbations that better reflect long-term drift, retaining controlled brightness variation to emulate sensor aging and scene temperature shifts while keeping the remainder of the YOLOv11 training recipe unchanged for stable optimization. On the 8-month LTDv2 benchmark, our approach achieves a weighted score of 0.448, improving over the official baseline score of 0.429 (by 4.4%), with an overall mAP@50 of 0.505. A configuration study on the validation split further characterizes the impact of key training choices. Temporal robustness analysis reveals pronounced seasonality, with peak performance in February (mAP@50 = 0.610) under cooler conditions and degradation in April (mAP@50 = 0.438) during seasonal transition. Our findings suggest that drift-aware, grayscale-consistent augmentation is a straightforward and effective lever for improving long-term LWIR detection, while temporal diagnostics are essential for understanding failure modes in real deployments.