BRDF-Based Photometric Stereo with AI Anomaly Detection for Fine Defect Inspection on Painted Electronic Buttons
Taesan Mo, Penghua Zhang, Seohyeon Jeong, Yeseul Kong, Gyuhae Park
TL;DR: A BRDF-based Photometric Stereo approach that accounts for complex reflectance characteristics when capturing fine geometric features of painted button surfaces, effectively enhancing the detectability of micro-defects that are visually indistinct in painted automotive components.
Ensuring the visual appearance quality of automotive interior buttons requires reliable inspection of painted surfaces. However, powder-coated finishes present complex reflectance behaviors, including directional glare and irregular highlight patterns, which often mask or resemble subtle defect features. To address this limitation, we explore a BRDF-based Photometric Stereo (PS) approach that accounts for complex reflectance characteristics when capturing fine geometric features of painted button surfaces. Leveraging BRDF-based modeling and multi-directional illumination, the PS method derives pixel-wise surface normals that are more robust to specular highlights and better represent true surface geometry. These surface normal maps are subsequently utilized as input to a deep learning model for detecting defective and anomalous surface regions. When integrated with AI-based anomaly detection, PS-derived geometric representations contributed to more reliable identification of fine surface irregularities, effectively enhancing the detectability of micro-defects that are visually indistinct in painted automotive components.