Photometric Stereo-Based Defect Detection System for Steel Components Manufacturing Using a Deep Segmentation Network

Sensors

Manuel Graña¹  Fátima A. Saiz²  Iñigo Barandiaran²  Ander Arbelaiz² 

[1] Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 20018 Donostia-San Sebastián, Spain;Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain;

关键词: photometric stereo;    quality control;    deep learning;    image processing;    semantic segmentation;   

DOI  :  10.3390/s22030882

来源: DOAJ

【 摘 要 】

This paper presents an automatic system for the quality control of metallic components using a photometric stereo-based sensor and a customized semantic segmentation network. This system is designed based on interoperable modules, and allows capturing the knowledge of the operators to apply it later in automatic defect detection. A salient contribution is the compact representation of the surface information achieved by combining photometric stereo images into a RGB image that is fed to a convolutional segmentation network trained for surface defect detection. We demonstrate the advantage of this compact surface imaging representation over the use of each photometric imaging source of information in isolation. An empirical analysis of the performance of the segmentation network on imaging samples of materials with diverse surface reflectance properties is carried out, achieving Dice performance index values above 0.83 in all cases. The results support the potential of photometric stereo in conjunction with our semantic segmentation network.

【 授权许可】

Unknown