Abstract
This study aims to develop a novel automated computer vision algorithm for quality inspection of surfaces with complex patterns. The proposed algorithm is based on both an autoencoder (AE) and a fully convolutional neural network (FCN). The AE is adopted for the self-generation of templates from test targets for defect detection. Because the templates are produced from the test targets, the position alignment issues for the matching operations between templates and test targets can be alleviated. The FCN is employed for the segmentation of a template into a number of coherent regions. Because the AE has the limitation that its capacities for the regeneration of each coherent region in the template may be different, the segmentation of the template by FCN is beneficial for allowing the inspection of each region to be independently carried out. In this way, more accurate detection results can be achieved. Experimental results reveal that the proposed algorithm has the advantages of simplicity for training data collection, high accuracy for defect detection, and high flexibility for online inspection. The proposed algorithm is therefore an effective alternative for the automated inspection in smart factories with a growing demand for the reliability for high quality production.
Highlights
Surface quality inspection is an important process in an industrial production system.Basic approaches for inspection are mostly by skilled inspectors, which may be timeconsuming and laborious
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
The training and testing images of the inspection targets for the neural network models are acquired from the industrial camera of the online surface inspection platform
Summary
Surface quality inspection is an important process in an industrial production system.Basic approaches for inspection are mostly by skilled inspectors, which may be timeconsuming and laborious. Surface quality inspection is an important process in an industrial production system. 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. 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]. A Haar–Weibull-variance model [5] has been found to be effective for the extraction of features for defect detection on strip steel surfaces. Some results are promising, the local abnormalities-based methods lack the effective use of existing normal-pattern data
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