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Surface Defect System for Long Product Manufacturing Using Differential Topographic Images.

F J delaCalle Herrero¹, Daniel F García¹, Rubén Usamentiaga¹

¹Department of Computer Science and Engineering, University of Oviedo, 33204 Gijón, Asturias, Spain.

Sensors (Basel, Switzerland)

|April 16, 2020

Summary

A new 3D laser reconstruction method offers fast, accurate surface defect detection for industrial products, meeting international standards. This automated system simplifies configuration and outperforms existing photometric systems.

Keywords:

computer vision defect detection long products rail surface

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Area of Science:

Industrial Manufacturing
Metrology
Computer Vision

Background:

Commercial surface inspection systems are costly and complex to configure.
Current methods often provide qualitative defect detection.
Industrial products require adherence to international quality standards.

Purpose of the Study:

To introduce a novel, automated surface defect detection method.
To enable defect detection based on international standard tolerances.
To develop a repeatable, manufacturer-configurable system.

Main Methods:

Utilizing 3D laser reconstruction for high-speed surface scanning.
Generating differential topographic images with direct dimensional translation.
Applying computer vision, statistical analysis, and a multilayer perceptron for defect identification and filtering.

Main Results:

Achieved high-speed, scan-by-scan comparison with desired product shapes.
Demonstrated superior performance over photometric systems in defect detection and false detection rates.
Validated the method on train track rails and a surface inspection rail pattern.

Conclusions:

The proposed 3D laser reconstruction method provides an efficient and accurate solution for industrial surface inspection.
The system meets international standards and offers a repeatable configuration process.
This approach significantly improves upon existing commercial surface inspection technologies.