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A Machine Vision Case Study Of U-Net Networks For Superficial Corrosion And Dirt Image Segmentation On Industrial Coated Steel Structures

A MACHINE VISION CASE STUDY OF U-NET NETWORKS FOR SUPERFICIAL CORROSION AND DIRT IMAGE SEGMENTATION ON INDUSTRIAL COATED STEEL STRUCTURES
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Convolutional deep neural networks are one of the main machine learning techniques applied to computer vision and object recognition tasks. Currently, they are very popular due to their proven effectiveness in solving image classification tasks and their significant theoretical and practical importance to the advancement of the deep learning field. Examples of successful image classification networks developed are AlexNet, VGG, and GoogLeNet.1,2,3 

Product Number: 51322-17995-SG
Author: Wesley Kaizer, Otavio Correa
Publication Date: 2022
Industry: Corrosion Monitoring and Control
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Corrosion detection in industrial assets and components is an important broad problem in the industries since it allows the temporal tracking of possible issues and the execution of preventive maintenance actions, such as protective coating. However, solving this problem using modern machine learning methods usually demands a careful design of artificial intelligence tools, such as neural networks, high computational resources for training and inference, and a large and adequate dataset. In this work we investigate the application of deep convolutional neural networks to the problem of image semantic segmentation of superficial corrosion and dirt present in mining industrial assets, using a set of images collected in place by corrosion inspectors and manually labeled by a data team. We compare two networks based on the popular U-Net model, in which one of them uses the transferred features from a pre-trained VGG-16 image classification model.

Corrosion detection in industrial assets and components is an important broad problem in the industries since it allows the temporal tracking of possible issues and the execution of preventive maintenance actions, such as protective coating. However, solving this problem using modern machine learning methods usually demands a careful design of artificial intelligence tools, such as neural networks, high computational resources for training and inference, and a large and adequate dataset. In this work we investigate the application of deep convolutional neural networks to the problem of image semantic segmentation of superficial corrosion and dirt present in mining industrial assets, using a set of images collected in place by corrosion inspectors and manually labeled by a data team. We compare two networks based on the popular U-Net model, in which one of them uses the transferred features from a pre-trained VGG-16 image classification model.

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