Detecting spills using thermal imaging, pretrained deep learning models, and a robotic platform
This addresses safety-critical spill detection for deployment on robotic platforms, though it is incremental as it applies existing methods to a specific domain.
The paper tackles real-time spill detection by combining pretrained deep learning models with RGB and thermal imaging, achieving up to 100% accuracy and inference times as low as 44 ms using lightweight models like VGG19 and NasNetMobile on a balanced dataset of 4,000 images.
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.