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

arXiv: https://arxiv.org/abs/2512.22263 · HTML: https://mlsanity.com/paper/2512.22263/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 24 Dec 2025.

## Builds on (most cited first, 25 of 25)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [A Survey of Deep Learning Techniques for Autonomous Driving](https://mlsanity.com/paper/1910.07738/) (2019)
- [Deep Multi-modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges](https://mlsanity.com/paper/1902.07830/) (2019)
- [Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey](https://mlsanity.com/paper/2112.08936/) (2021)
- [Multi-modal Sensor Fusion for Auto Driving Perception: A Survey](https://mlsanity.com/paper/2202.02703/) (2022)
- [Illumination-aware Faster R-CNN for Robust Multispectral Pedestrian Detection](https://mlsanity.com/paper/1803.05347/) (2018)
- [Multispectral Pedestrian Detection via Simultaneous Detection and Segmentation](https://mlsanity.com/paper/1808.04818/) (2018)
- [A Survey on Segment Anything Model (SAM): Vision Foundation Model Meets Prompt Engineering](https://mlsanity.com/paper/2306.06211/) (2023)
- [RGB-D And Thermal Sensor Fusion: A Systematic Literature Review](https://mlsanity.com/paper/2305.11427/) (2023)
- [When Geoscience Meets Foundation Models: Towards General Geoscience Artificial Intelligence System](https://mlsanity.com/paper/2309.06799/) (2023)
- [Multi-Modal Lidar Dataset for Benchmarking General-Purpose Localization and Mapping Algorithms](https://mlsanity.com/paper/2203.03454/) (2022)
- [D3T: Distinctive Dual-Domain Teacher Zigzagging Across RGB-Thermal Gap for Domain-Adaptive Object Detection](https://mlsanity.com/paper/2403.09359/) (2024)
- [Alignment-Free RGB-T Salient Object Detection: A Large-scale Dataset and Progressive Correlation Network](https://mlsanity.com/paper/2412.14576/) (2024)
- [A Multispectral Automated Transfer Technique (MATT) for machine-driven image labeling utilizing the Segment Anything Model (SAM)](https://mlsanity.com/paper/2402.11413/) (2024)
- [There is no SAMantics! Exploring SAM as a Backbone for Visual Understanding Tasks](https://mlsanity.com/paper/2411.15288/) (2024)
- [Learning A Robust RGB-Thermal Detector for Extreme Modality Imbalance](https://mlsanity.com/paper/2505.22154/) (2025)
- [Optimizing Multispectral Object Detection: A Bag of Tricks and Comprehensive Benchmarks](https://mlsanity.com/paper/2411.18288/) (2024)
- [Multispectral Object Detection with Deep Learning](https://mlsanity.com/paper/2102.03115/) (2021)
- [Mirror Complementary Transformer Network for RGB-thermal Salient Object Detection](https://mlsanity.com/paper/2207.03558/) (2022)
- [YOLOv11-RGBT: Towards a Comprehensive Single-Stage Multispectral Object Detection Framework](https://mlsanity.com/paper/2506.14696/) (2025)
- [MCOD: The First Challenging Benchmark for Multispectral Camouflaged Object Detection](https://mlsanity.com/paper/2509.15753/) (2025)
- [SAMSOD: Rethinking SAM Optimization for RGB-T Salient Object Detection](https://mlsanity.com/paper/2510.03689/) (2025)
- [RGB-T Object Detection via Group Shuffled Multi-receptive Attention and Multi-modal Supervision](https://mlsanity.com/paper/2405.18955/) (2024)
- [AutoDetect: Designing an Autoencoder-based Detection Method for Poisoning Attacks on Object Detection Applications in the Military Domain](https://mlsanity.com/paper/2509.03179/) (2025)

## Built on by (most cited first, 1 of 1)

- [Seeing the Unseen: Camouflaged Object Detection Beyond the Visible Spectrum](https://mlsanity.com/paper/2608.30355/) (2026)

## Abstract (arXiv)

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.