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.
Abstract (translated)
这篇论文介绍了一个实时漏油检测系统，该系统利用预训练的深度学习模型结合RGB和热成像技术来区分泄漏与非泄漏场景，并且可以在各种环境中应用。通过使用一个平衡的二元数据集（4,000张图像），我们的实验展示了热成像在推理速度、准确性以及模型大小方面的优势。我们能够达到高达100%的准确率，所使用的轻量级模型包括VGG19和NasNetMobile，其中基于热成像的模型在不同的光照条件下表现出更快的速度和更强的鲁棒性。 我们的系统可以在消费级硬件（如RTX 4080）上运行，并且可以达到低至44毫秒的推理时间以及低于350MB的模型大小，这突显了其在安全关键环境中的部署能力。通过与实际机器人及测试数据集进行实验的结果表明，基于热成像训练的VGG19模型表现最佳。
URL
https://arxiv.org/abs/2510.08770
