Menu
Search
Filters
Close
Menu

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
Available for download

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
$0.00
$20.00
$20.00

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.

Product tags
Also Purchased
Picture for A Remote Monitoring System Featuring GPS Synchronized Instant-Off Potential And Line-Current Measurements
Available for download

A Remote Monitoring System Featuring GPS Synchronized Instant-Off Potential And Line-Current Measurements

Product Number: 51322-17635-SG
Author: Andreas Junker Olesen, Lars Vendelbo Nielsen
Publication Date: 2022
$20.00

There are several ways to validate the performance of a cathodic protection (CP) system for buried pipelines. Over the years, pipeline networks and their corrosion challenges have become increasingly complicated, not least due to the many sources of both AC and DC interference that affects CP operation. Also, the various measurement techniques that can be applied to test CP effectiveness has increased over the years. Finally, the sheer number of buried pipeline miles has been constantly increasing.
Picture for A Further Look At The Impacts Of Corrosion Inhibitor On Scale Prevention
Available for download

A Further Look At The Impacts Of Corrosion Inhibitor On Scale Prevention

Product Number: 51322-17676-SG
Author: Qiwei Wang, Tao Chen, Hameed Al-Badairy, Sultan Alsubaie, Yasser Al-Jeshi, Mohammad Alsidan
Publication Date: 2022
$20.00

Scale and corrosion inhibitors are commonly used in many oil and gas production systems to prevent inorganic deposition and to protect asset integrity. Scale inhibitor products are based on organic compounds with phosphate or carboxylic functional groups such as amino phosphonates, phosphate esters, phosphino polymers, polycarboxylate and polysulfonates,1 as shown in Figure 1. These anionic groups have strong affinity to alkaline earth cations and can adsorb on the active growth sites of scale crystal (Figure 2), resulting in stopping or delaying the scale formation process.
Picture for A Case Study-Caustic Gouging Of Boiler Tubes
Available for download

A Case Study-Caustic Gouging Of Boiler Tubes

Product Number: 51322-17780-SG
Author: Suresh Divi, Sri Krishna Chimbli
Publication Date: 2022
$20.00

Caustic corrosion is sometimes referred to as “caustic attack or “caustic gouging.” Corrosion of this type may result from internally fouled heat transfer surfaces and the presence of sodium hydroxide in the boiler water; and concentrated solutions of alkali where the normal washing of the tube metal ID is restricted after Departure from Nucleate Boiling (DNB), i.e., when the steam bubble release exceeds the rinsing rate.
Categories
Industry
Recently viewed
Popular tags
View all

Association for Materials Protection and Performance

© AMPP All Rights Reserved.
Membership Terms of Content Use
Contact Customer Support