Many manufacturing environments operate in low-light conditions or within enclosed machines where conventional vision systems struggle. Infrared cameras provide complementary advantages in such environments. Simultaneously, supervised AI systems require large labeled datasets, which makes zero-shot learning frameworks more practical for applications including infrared cameras. Recent advances in vision-language foundation models (VLMs) offer a new path in zero-shot predictions from paired image-text representations. However, current VLMs cannot understand infrared camera data since they are trained on RGB data. This work introduces VLM-IRIS (Vision-Language Models for InfraRed Industrial Sensing), a zero-shot framework that adapts VLMs to infrared data by preprocessing infrared images captured by a FLIR Boson sensor into RGB-compatible inputs suitable for CLIP-based encoders. We demonstrate zero-shot workpiece presence detection on a 3D printer bed where temperature differences between the build plate and workpieces make the task well-suited for thermal imaging. VLM-IRIS converts the infrared images to magma representation and applies centroid prompt ensembling with a CLIP ViT-B/32 encoder to achieve high accuracy on infrared images without any model retraining. These findings demonstrate that the proposed improvements to VLMs can be effectively extended to thermal applications for label-free monitoring.

翻译：许多制造环境在低光照条件下或封闭设备内部运行，传统视觉系统在此类场景中面临挑战。红外相机在此类环境中具有互补性优势。同时，监督式人工智能系统需要大量标注数据集，这使得零样本学习框架在包括红外相机在内的应用中更具实用性。视觉-语言基础模型的最新进展为基于配对图像-文本表征的零样本预测提供了新途径。然而，当前视觉-语言模型因训练数据为RGB格式而无法理解红外相机数据。本研究提出VLM-IRIS（面向红外工业传感的视觉-语言模型），该零样本框架通过将FLIR Boson传感器采集的红外图像预处理为适用于基于CLIP编码器的RGB兼容输入，使视觉-语言模型适配红外数据。我们在3D打印平台上演示了零样本工件存在检测任务，其中构建板与工件之间的温差使该任务特别适合热成像技术。VLM-IRIS将红外图像转换为岩浆色表征，并采用质心提示集成方法与CLIP ViT-B/32编码器结合，在无需模型重训练的情况下实现对红外图像的高精度识别。这些发现表明，所提出的视觉-语言模型改进方案可有效扩展至热成像应用领域，实现无标注监测。