Based on Deep Convolutional Neural Network and Machine Vision Applied to the Surface Defect Detection of Hard Disk Metal Gaskets

Chao-Ching Ho, Wei-Ming Su, Sankarsan Mohanty

Abstract

This Study aims at the surface defects of aluminum gaskets as the detection targets. The types of defects are yellow spots, incomplete grinding and bump damages. The detection method will select image processing or deep learning according to the characteristics of the defects. The characteristic of yellow spots has many variables of random shapes and different shades of color, it is difficult to use image processing to detect defects, therefore, this Study selects deep learning as the detection method of yellow spot and the detection network architecture is a modified architecture based on U-Net. It also proposes the preprocess of removing the background of the image before the model training, by removing the outer pixel value out side the gasket area on the image. It was found that the preprocess can improve the Intersection over Union (IoU) by 0.041. The experiment results showes that using the proposed network architecture the evaluation of yellow spot IoU is 0.611 which is better than the original U-Net with a model accuracy of 99.56%.

Keywords

Deep Convolutional Networks; Automated Optical Inspection; Digital Image Processing; Metal Gaskets; Defect Detection

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IMEKO-TC10-2022-007.pdf

DOI

10.21014/tc10-2022.007

IMEKO TC

TC10 - Measurement for Diagnostics, Optimization and Control

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