Copper Strip Surface Defect Detection Model Based on Deep Convolutional Neural Network

Yanghuan Xu,Bowei Duan,Dongcheng Wang,Hongmin Liu,Huaxin Yu

doi:10.3390/app11198945

Abstract

Surface defect automatic detection has great significance for copper strip production. The traditional machine vision for surface defect automatic detection of copper strip needs artificial feature design, which has a long cycle, and poor ability of versatility and robustness. However, deep learning can effectively solve these problems. Therefore, based on the deep convolution neural network and the transfer learning strategy, an intelligent recognition model of surface defects of copper strip is established in this paper. Firstly, the defects were classified in accordance with the mechanism and morphology, and the surface defect dataset of copper strip was established by comprehensively adopting image acquisition and image augmentation. Then, a two-class discrimination model was established to achieve the accurate discrimination of perfect and defect images. On this basis, four CNN models were adopted for the recognition of defect images. Among these models, the EfficientNet model through transfer learning strategy had the best comprehensive performance with a recognition accuracy rate of 93.05%. Finally, the interpretability and deficiency of the model were analysed by the class activation map and confusion matrix, which point toward the direction of further optimization for future research.

Highlights

Copper strip is the typical high-end product in the nonferrous metals field, which is widely used in new-energy vehicles, aerospace, and precision electronic equipment [1,2].The surface quality is one of the most important quality indicators of the copper strip, which seriously affects the appearance and yield of products, but may have adverse effects on downstream processes [3,4]
Achieving the rapid and accurate classification of copper strip surface defects is remarkably important for improving product quality
Manual visual inspection is still widely used for surface defect detection of copper strip during industrial production despite its low recognition accuracy, poor stability and high labour intensity [5,6]

Summary

Introduction

Copper strip is the typical high-end product in the nonferrous metals field, which is widely used in new-energy vehicles, aerospace, and precision electronic equipment [1,2].The surface quality is one of the most important quality indicators of the copper strip, which seriously affects the appearance and yield of products, but may have adverse effects on downstream processes [3,4]. Manual visual inspection is still widely used for surface defect detection of copper strip during industrial production despite its low recognition accuracy, poor stability and high labour intensity [5,6]. Zhang et al [8] extracted three features (colour, brightness, and orientation) of copper strip surface defects through Gaussian pyramid decomposition and Gabor filters and established a Markov classification model to achieve defect classification. Meng [10] proposed the MM–Canny defect segmentation algorithm based on the improved Canny edge detection operator and morphology method and established a support vector machine classification model to achieve defect classification by extracting three feature types of geometry (area and diameter ratio of length and short), grey (average grey, variance, slope, and defect area energy), and texture (corner second-order matrix, contrast, correlation, and entropy). Zhang et al [11]

Methods

Results

Conclusion