Introducing a tool for synthetic defect image data generation: enhancing industrial surface inspection

Visual surface defect detection in complex scenarios remains a manual endeavor in industrial inspection. Stateof- the-art machine learning approaches promise to automate this task through machine vision; however, they require substantial, suitable training data to be effectively trained. For industrial products, such data are often unavailable for several reasons, including the rarity of certain defects or their manifestations. This paper introduces a tool to generate synthetic data for training machine vision systems using rendering techniques. Our tool allows for targeted manipulation of process parameters throughout the rendering chain, including scene composition involving object, camera, and lighting, defect generation, placement, and material generation. This enables an exploration of the impacts along the rendering pipeline and facilitates selecting an appropriate data set for specific inspection tasks. We demonstrate our tool’s effectiveness by applying models trained on our synthetic data to a real-world inspection task.

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DDC Class

620: Engineering

006: Special computer methods

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http://rightsstatements.org/vocab/InC/1.0/

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Copyright 2025 Society of Photo‑Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this publication for a fee or for commercial purposes, and modification of the contents of the publication are prohibited.

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Introducing a tool for synthetic defect image data generation: enhancing industrial surface inspection