Evaluating an Adaptive Multispectral Turret System for Autonomous Tracking Across Variable Illumination Conditions

TL;DR

This paper introduces an adaptive RGB-LWIR fusion system for autonomous robots that dynamically optimizes detection performance across various lighting conditions, significantly outperforming baseline models.

Contribution

We developed a multi-ratio fusion framework and trained multiple YOLO models to enhance detection in variable illumination environments, a novel approach in multispectral robotic vision.

Findings

Best full-light model achieved 92.8% confidence

Dim-light fusion reached 92.0% confidence

No-light fusion exceeded baseline performance with 71.0% confidence

Abstract

Autonomous robotic platforms are playing a growing role across the emergency services sector, supporting missions such as search and rescue operations in disaster zones and reconnaissance. However, traditional red-green-blue (RGB) detection pipelines struggle in low-light environments, and thermal-based systems lack color and texture information. To overcome these limitations, we present an adaptive framework that fuses RGB and long-wave infrared (LWIR) video streams at multiple fusion ratios and dynamically selects the optimal detection model for each illumination condition. We trained 33 You Only Look Once (YOLO) models on over 22,000 annotated images spanning three light levels: no-light (<10 lux), dim-light (10-1000 lux), and full-light (>1000 lux). To integrate both modalities, fusion was performed by blending aligned RGB and LWIR frames at eleven ratios, from full RGB (100/0) to full LWIR (0/100) in 10% increments. Evaluation showed that the best full-light model (80/20 RGB-LWIR) and dim-light model (90/10 fusion) achieved 92.8% and 92.0% mean confidence; both significantly outperformed the YOLOv5 nano (YOLOv5n) and YOLOv11 nano (YOLOv11n) baselines. Under no-light conditions, the top 40/60 fusion reached 71.0%, exceeding baselines though not statistically significant. Adaptive RGB-LWIR fusion improved detection confidence and reliability across all illumination conditions, enhancing autonomous robotic vision performance.