Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description

• URL: http://arxiv.org/abs/2512.11098v1
• Date: Thu, 11 Dec 2025 20:20:38 GMT
• Title: Vision-Language Models for Infrared Industrial Sensing in Additive Manufacturing Scene Description
• Abstract summary: Infrared cameras provide advantages in manufacturing environments in low-light conditions or enclosed machines where vision systems struggle.<n>Recent advances in vision-language systems (VLMs) offer new in-text predictions in relation to RGB data.<n>This work introduces a zero-shot encoder that adapts VLMs to infrared data by preprocessing images captured by a FLIR Boson camera.
• Score: 1.7786984823452563
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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