Uneven illumination surface defects inspection based on convolutional neural network

TL;DR

This paper presents a convolutional neural network approach for surface defect inspection that effectively handles uneven illumination, improving accuracy and automation over traditional methods.

Contribution

It introduces a CNN-based method with structural adjustments and training parameter tuning to detect surface defects under uneven lighting conditions.

Findings

CNN automatically learns features without preprocessing

Accurately identifies various defects under uneven illumination

Outperforms traditional machine vision inspection methods

Abstract

Surface defect inspection based on machine vision is often affected by uneven illumination. In order to improve the inspection rate of surface defects inspection under uneven illumination condition, this paper proposes a method for detecting surface image defects based on convolutional neural network, which is based on the adjustment of convolutional neural networks, training parameters, changing the structure of the network, to achieve the purpose of accurately identifying various defects. Experimental on defect inspection of copper strip and steel images shows that the convolutional neural network can automatically learn features without preprocessing the image, and correct identification of various types of image defects affected by uneven illumination, thus overcoming the drawbacks of traditional machine vision inspection methods under uneven illumination.