Automated Surface Defect Inspection Based on ...

1. Introduction

Surface quality inspection is an important process in an industrial production system. Basic approaches for inspection are mostly by skilled inspectors, which may be time-consuming and laborious. Furthermore, it would be difficult to meet the requirements of reliability and robustness. With the advent of computer vision [ 1 ] and artificial intelligence techniques [ 2 ], automated computer visual inspection methods are found to be beneficial for improving performance for industrial production.

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One way to carry out surface inspection is by analyzing textures to find patterns without normal features on the test targets. When the surface texture distribution is known a priori, the features associated with local abnormalities can be extracted [ 3 4 ]. For example, a Haar&#;Weibull-variance model [ 5 ] has been found to be effective for the extraction of features for defect detection on strip steel surfaces. In frequency domain, spectral features are usually extracted by Fourier transform [ 6 ]. Although some results are promising, the local abnormalities-based methods lack the effective use of existing normal-pattern data. The occurrence of false alarms is likely. Some alternative approaches take normal and/or abnormal patterns into consideration [ 7 10 ] by deep convolutional neural networks (CNN). For applications such as building defect detection, high classification accuracy can be achieved [ 10 ]. The limitation of these methods is that the number of training samples should be adequate and balanced enough to achieve a desirable performance. However, for scenarios where defective samples are scarce, effective training for a CNN may be a challenging task.

Template-based methods can be employed for alleviating the requirements for the collection of defective samples for surface inspection. The methods introduce defect-free template images into the detection procedure so that no prior knowledge on defects is required. Basic template-based approaches accomplish defect detection by measuring the similarity (or dissimilarity) between the given test image and defect-free template. The normalized cross correlation is classical for dissimilarity measurement. Its improved versions have been proposed, including the partial information correlation coefficient [ 11 ] and asymmetric correlation [ 12 ]. The distribution-based template establishment procedure [ 13 ] is also found to be effective for enhancing detection accuracy. A common drawback of some template matching approaches is that proper alignment between the test image and template is desired for the correlation computation. However, for many applications, the enforcement of alignment operations may be difficult, resulting in degradation of detection accuracy. An alternative template-based method is to adopt the template images as the training images for an autoencoder (AE) for dimension reduction and feature extraction [ 14 15 ]. Defect detection can be accomplished by simply comparing the input and output of the AE. No precise alignment is required before the inspection. The accuracy can be further improved by carrying out the AE-based reconstruction in a multiscale fashion [ 16 ].

A target surface to be inspected can usually be viewed as an image consisting of a number of coherent regions, where each region is a set of connected pixels sharing common characteristics such as texture or color [ 17 18 ]. Although the AEs are promising for surface quality inspection, they may only be suited for surfaces with only a single coherent region. For many real-world applications, inspection of surfaces with multiple coherent regions is usually desired. Because different regions may have different features, it would then be difficult for an AE to extract a feature match to all the regions. As a result, the AE may have different capabilities for each region for defect detection. A unified approach for surface inspection over different homogeneous regions may result in high miss rates in some regions and/or high false alarm rates in others.

The objective of this paper is to develop a novel automated computer vision algorithm for quality inspection of surfaces with multiple coherent regions. The proposed algorithm is a template-based algorithm for defect detection. The algorithm contains two neural networks. The first network is an AE for template generation of an input test target. The second network is a fully convolutional network (FCN) [ 19 20 ] for the segmentation of the template into a number of homogeneous regions. Each region of the template is then compared with the corresponding region of the test target for the surface inspection. Because different regions have different features, each region is inspected independently according to its own criteria, different from the other ones. In this way, defects can be accurately identified on all the regions.

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The proposed algorithm has a number of advantages. First of all, it does not need defective patterns as training samples. Only a small number of normal surface patterns may suffice for training. A data augmentation scheme is adopted for the generation of defective images. This could facilitate the training operations. It is especially beneficial for cases when the collection of defective samples is difficult, and/or there is no prior knowledge about the surface defects. Furthermore, it is not necessary to carry out the inspection with precise alignment to the template by the proposed algorithm. The surface inspection process can then be effectively simplified.

The final and the most important feature is that the proposed algorithm is able to achieve high detection accuracy even when multiple coherent regions are presented on the surface. Because each region can be independently inspected for attaining the optimal accuracy, the proposed algorithm is beneficial for providing reliable and effective defect detection over surfaces of large varieties of objects.

The novelty and contribution of this work is to propose a novel algorithm combining both AE and FCN for defect detection. Most of the existing AE-based approaches [ 14 16 ] detect defects from the reproduced images by AEs in a unified manner. By contrast, our method is able to separate reproduced templates into different regions by FCN and inspect each region independently. To improve segmentation accuracy, a novel two-stage training process is presented, where the first stage and the second stage are for AE and FCN, respectively. The defects are regarded as noises in our model. The training at the first stage takes the denoising processes into consideration so that the AE is able to remove defects for template generation. The second stage training is based on the training results from the first stage so that templates can be accurately segmented. The proposed technique provides higher flexibility and better accuracy for defect detection. Furthermore, the technique may also be beneficial for other detection applications such as slug velocity detection in microchannels [ 21 ].

The remaining parts of this study are organized as follows. Section 2 presents the proposed automated surface inspection algorithm in detail. Experimental results of the proposed algorithm are then presented in Section 3 . Finally, Section 4 includes some concluding remarks of this work.

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