Vision system with deep learning classifiers for automatic quality inspection

Abstract

The role of data analytics in industrial quality inspection has grown rapidly in the last few years. This has also instigated increasing interests in the development of analytical, data driven models based on machine learning techniques with vision systems in industrial quality inspection. The deep learning employs computational models which are composed of a series of transforming and processing layers to learn representations of data with multiple levels of abstraction. In this paper, we present a novel approach combining deep convolutional neural networks (CNN) and National Instruments (NI) vision system for image classification to detect cracks on product surfaces. To mimic cracks on product surfaces, 15 various samples with and without cracks were designed in a 3D computer-aided design (CAD) solid modeling software and constructed using a 3D printer. In data acquisition, these samples with dimensions of 10 x 10 x 1 cm were systematically imaged using CCD camera and NI vision system, and approximately 1000 images obtained. Acquired images were input to two CNN classifiers, AlexNet and GoogLeNet architectures. The CNN automatically learns a hierarchy of increasingly complex features and a related classifier directly from training data sets. For AlexNet architecture, the CNN employs 5 convolutional and 3 max pooling layers with 3 fully connected layers to perform the image classification. For GoogLeNet architecture, the CNN utilizes 22 layers with multiple inception modules and SoftMax classification layers. The preliminary results demonstrate the feasibility of using deep learning algorithms with NI vision system to perform the automated industrial quality inspection.