Surface roughness evaluation based on near point lighting photometric stereo

Surface roughness evaluation is crucial for enhancing surface properties. Traditional offline human-assisted inspection techniques are labor-intensive and time-consuming, hindering manufacturing productivity. Current machine vision-based roughness measurement methods primarily rely on grayscale or color images, neglecting the benefits of 3D morphology's multidimensional information. This results in complex and inaccurate algorithms. To address these issues, we propose an efficient surface roughness evaluation method using Near Point Lighting Photometric Stereo (NPL-PS). Our NPL-PS scheme, configured with one camera and 24 LED point light sources, generates a 3D depth map of the machined surface. Under the classic Lambertian model and camera weak perspective transform, we derive a linearized photometric equation for NPL-PS containing light intensity attenuation. By solving the linear equation using our proposed methods for illumination position calibration and non-uniform illumination correction, we estimate the normal vector corresponding to each pixel. We then use the Frankot-Chellapa algorithm to recover the height map from the vector field. Finally, we extract roughness texture features from the height map using polynomial fitting and quantitatively evaluate the roughness. Our experiments using real surface roughness specimens demonstrate the feasibility and effectiveness of this method compared to traditional methods, laying the foundation for subsequent practical production applications.

Automated summary

Surface roughness evaluation is crucial for enhancing surface properties. Current machine vision-based roughness measurement methods neglect the benefits of 3D morphology, resulting in complex and inaccurate algorithms. To address this, we propose an efficient surface roughness evaluation method using Near Point Lighting Photometric Stereo (NPL-PS).