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.

Hello, I'm the author of the Real-Time Voice Cloning project that was posted here recently. I've developped a spin-off of the deep voice encoder from that project in order to perform speaker diarization, modest fake speech detection, voice comparison and high level voice feature extraction. It comes as a lightweight python package (pip install resemblyzer) for which you will find demos here. I hope you will find it interesting!