{"ID":2858424,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.08770","arxiv_id":"2510.08770","title":"Detecting spills using thermal imaging, pretrained deep learning models, and a robotic platform","abstract":"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.","short_abstract":"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 inferenc...","url_abs":"https://arxiv.org/abs/2510.08770","url_pdf":"https://arxiv.org/pdf/2510.08770v1","authors":"[\"Gregory Yeghiyan\",\"Jurius Azar\",\"Devson Butani\",\"Chan-Jin Chung\"]","published":"2025-10-09T19:40:58Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.LG\",\"cs.RO\"]","methods":"[]","has_code":false}