Automating Visual Inspection with Convolutional Neural Networks

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Published Sep 22, 2019
Sreerupa Das Christopher D Hollander Suraiya Suliman

Abstract

Convolutional Neural Networks (CNNs) have become the recent tool of choice for many visual detection tasks, including object classification, localization, detection, and segmentation. CNNs are specialized neural networks composed of many layers and specifically designed to analyze grid-like data, e.g. images. One of the key features of a CNN is its ability to automatically detect important features within an image (e.g. edges, patterns, shapes); prior to CNNs, these features had to be manually engineered by subject matter experts.

Inspired by the significant achievements and success that CNNs have experienced in the domain of computer vision, we examine a specific convolutional neural network (CNN) architecture, U-Net, suited for the task of visual defect detection. We identify and discuss situations for the use of this architecture in the specific context of external defect detection on aircraft and experimentally discuss its performance across a dataset of common visual defects.

One requirement of training Convolution Networks on an image analysis task is the need for a large image (training) data set.  We address this problem by using synthetically generated images from computer models of jets with varying angles and perspectives with and without induced faults in the generated images.  This paper presents the initial results of using CNNs, specifically U-Net, to detect aerial vehicle surface defects of three categories.  We further demonstrate that CNNs trained on synthetic images can then be used to detect faults in real images of jets with visual damages.  The results obtained in this research, indicate that our approach has been quite effective in detecting surface anomalies in our tests.

How to Cite

Das, S., Hollander, C. D., & Suliman, S. (2019). Automating Visual Inspection with Convolutional Neural Networks. Annual Conference of the PHM Society, 11(1). https://doi.org/10.36001/phmconf.2019.v11i1.868
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Keywords

Convolution Neural Networks, Defect Detection, Semantic segmentation, U-Net

Section
Technical Papers