# Detecting spills using thermal imaging, pretrained deep learning models, and a robotic platform

arXiv: https://arxiv.org/abs/2510.08770 · HTML: https://mlsanity.com/paper/2510.08770/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 10 references that are in the map.
- First posted on arXiv 9 Oct 2025.

## Builds on (most cited first, 2 of 2)

- [Pipeline Leak Detection Systems and Data Fusion: A Survey](https://mlsanity.com/paper/1902.03927/) (2019)
- [Liquid Leak Detection Using Thermal Images](https://mlsanity.com/paper/2312.10980/) (2023)

## Abstract (arXiv)

This paper presents a real-time spill detection system that utilizes pretrained deep learning models with RGB and thermal imaging to classify spill vs. no-spill scenarios across varied environments. Using a balanced binary dataset (4,000 images), our experiments demonstrate the advantages of thermal imaging in inference speed, accuracy, and model size. We achieve up to 100% accuracy using lightweight models like VGG19 and NasNetMobile, with thermal models performing faster and more robustly across different lighting conditions. Our system runs on consumer-grade hardware (RTX 4080) and achieves inference times as low as 44 ms with model sizes under 350 MB, highlighting its deployability in safety-critical contexts. Results from experiments with a real robot and test datasets indicate that a VGG19 model trained on thermal imaging performs best.