An intelligent machine vision system for detecting surface defects on packing boxes based on support vector machine

Yu Wu,Yanjie Lu

doi:10.1177/0020294019858175

Yu Wu, Yanjie Lu

Open Access

https://doi.org/10.1177/0020294019858175

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Abstract

Defects in product packaging are one of the key factors that affect product sales. Traditional defect detection depends primarily on artificial vision detection. With the rapid development of machine vision, image processing, pattern recognition, and other technologies, industrial automation detection has become an inevitable trend because machine vision technology can greatly improve accuracy and efficiency; therefore, it is of great practical value to study automatic detection technology of the surface defects encountered in packaging boxes. In this study, machine vision and machine learning were combined to examine a surface defect detection method based on support vector machine where defective products are eliminated by a sorting robot system. After testing, the support vector machine training model using radial basis function kernel detects three kinds of defects at the same time under the ideal condition of parameter selection, and the effective detection rate is 98.0296%.

Highlights

The printing quality of the packing box can improve customer recognition of a product
This paper introduces a product packaging box defect detection method combining image processing technology and support vector machine (SVM).[1,2,3,4]
The results showed that the detection method using machine learning has a relative improvement in the rate of correctness and speed compared with the traditional pattern recognition technology

Summary

Introduction

The printing quality of the packing box can improve customer recognition of a product. From the original image to the accurate identification of defects, quality is divided into two parts: identification of the types of defects based on the basic image processing technology and the use of SVM training set to match the results to improve recognition speed and accuracy. It does not involve the probability measure and the law of large numbers, so it is different from the existing statistical methods In essence, it avoids the traditional process from generalization to deduction, and achieves efficient transduction inference from training samples to prediction samples, which greatly simplifies the classification and regression problems. In order to detect surface defects, we first use the same type (size) of different defect image samples to train SVM models so as to construct a multi-class SVM classifier

Structural training samples

Extraction of characteristic parameters

Training SVM

Findings

Experiments and discussion

Conclusion