用热成像和预训练模型实现实时溢出检测，效果快且准。
Detecting spills using thermal imaging, pretrained deep learning models, and a robotic platform
- 结合热成像与预训练模型，实时区分有无溢出。
- 热成像模型准确率达100%，推理速度最快仅44毫秒。
- 适合在光照复杂场景部署，硬件要求低。
本文提出一种基于预训练深度学习模型的实时溢出检测系统，融合RGB与热成像数据，在不同环境下分类溢出与非溢出场景。使用包含4000张图像的平衡二元数据集，实验表明热成像在推理速度、准确率和模型尺寸方面具有优势。采用轻量级模型如VGG19和NasNetMobile，热成像模型在不同光照条件下表现更快更稳健，准确率最高达100%。系统在消费级硬件（RTX 4080）上运行，推理时间低至44毫秒，模型大小低于350 MB，具备在安全关键场景中部署的潜力。机器人实测及测试数据集结果表明，基于热成像训练的VGG19模型表现最优。
原文摘要 · Abstract (English)
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.
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