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Item type:Publication, Access status: with files , Tuning domain randomization for 3D-rendered synthetic training data for defect detection in aircraft engine borescope inspectionVisual surface defect detection in complex industrial inspection scenarios remains predominantly manual. Deep learning–based vision systems promise to automate this task but require large quantities of representative, annotated defect images, which are often unavailable due to the rarity and diversity of relevant defects. We address this challenge with a modular synthetic data generation pipeline leveraging physically based 3D rendering, which enables systematic domain randomization throughout the entire rendering chain. The pipeline comprises dedicated modules for scene composition, geometry-based defect generation, procedural material modeling, rendering, and automatic pixel-wise annotation, allowing users to expose and tune key parameters such as defect size, position, and penetration depth, object materials, camera poses, and lighting. We first provide a structured analysis of all process steps that influence image appearance in defect synthesis and then instantiate the pipeline for a case study on aircraft engine borescope inspection, targeting missing-material defects on high-pressure compressor blades. Using a two-stage experimental design, we employ a Plackett–Burman screening over 22 candidate factors followed by a one-factor-at-a-time optimization of the most influential parameters. Our results show that the material model, defect positioning, defect size, and penetration depth randomization dominate detection performance. Training an object detector solely on synthetic images generated with the optimized configuration yields a maximum of 0.89 mAP@0.5IoU on a real-world borescope validation set, with performance saturating at ∼500 rendered training images. These findings demonstrate that carefully structured domain randomization in a physically based rendering pipeline can provide practically useful synthetic training data for industrial defect detection.Publicationtype: Journal Article9 13 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Introducing a tool for synthetic defect image data generation: enhancing industrial surface inspectionVisual 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.Publicationtype: Conference Paper77 75 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: with files , Procedural synthetic training data generation for AI-based defect detection in industrial surface inspectionSupervised machine learning methods are increasingly used for detecting defects in automated visual inspection systems. However, these methods require large quantities of annotated image data of the surface being inspected, including images of defective surfaces. In industrial contexts, it is difficult to collect the latter since acquiring sufficient image data of defective surfaces is costly and time-consuming. Additionally, gathered datasets tend to contain selection-bias, e.g. under representation of certain defect classes, and therefore result in insufficient training data quality. Synthetic training data is a promising alternative as it can be easily generated unbiasedly and in large quantities. In this work, we present a procedural pipeline for generating training data based on physically based renderings of the object under inspection. Defects are being introduced as 3D-models on the surface of the object. The generator provides the ability to randomize object and camera parameters within given intervals, allowing the user to use the domain randomization technique to bridge the domain gap between the synthetic data and the real world. Experiments suggest that the data generated in this way can be beneficial to training defect detection models.Publicationtype: Journal ArticleScopus© Citations 12 242 333