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

Fátima A Saiz^1,2, Iñigo Barandiaran¹, Ander Arbelaiz¹

¹Vicomtech Foundation, Basque Research and Technology Alliance (BRTA), Mikeletegi 57, 20009 Donostia-San Sebastián, Spain.

Sensors (Basel, Switzerland)

|February 15, 2022

Summary

This study introduces an automated system for metallic component quality control using photometric stereo and semantic segmentation. The novel approach enhances defect detection accuracy by combining surface data into a compact RGB image representation.

Keywords:

deep learning image processing photometric stereo quality control semantic segmentation

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Area of Science:

Materials Science
Computer Vision
Metrology

Background:

Automated quality control is crucial for manufacturing efficiency and product reliability.
Traditional defect detection methods can be labor-intensive and subjective.
Integrating advanced imaging and machine learning offers potential for improved accuracy and consistency.

Purpose of the Study:

To develop an automated system for metallic component quality control.
To leverage photometric stereo and semantic segmentation for defect detection.
To create a compact surface representation for enhanced defect identification.

Main Methods:

A photometric stereo-based sensor was employed for surface data acquisition.
A customized semantic segmentation network was developed for defect detection.
Photometric stereo images were combined into a single RGB image for input to the network.

Main Results:

The system achieved Dice performance index values above 0.83 across various material reflectances.
The compact RGB surface representation outperformed individual photometric stereo image sources.
The system successfully demonstrated potential for automatic surface defect detection.

Conclusions:

The proposed system offers an effective solution for automated metallic component quality control.
Combining photometric stereo data into a compact representation enhances defect detection performance.
The integration of photometric stereo and semantic segmentation shows significant promise for industrial applications.