Search research articles

Related Concept Videos

Corrosion

Corrosion

The degradation of metals due to natural electrochemical processes is known as corrosion. Rust formation on iron, tarnishing of silver, and the blue-green patina that develops on copper are examples of corrosion. Corrosion involves the oxidation of metals. Sometimes it is protective, such as the oxidation of copper or aluminum, wherein a protective layer of metal oxide or its derivatives forms on the surface, protecting the underlying metal from further oxidation. In other cases, corrosion is...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

A New Multi-Modal Data Fusion Framework for Delamination Detection in Concrete Bridge Decks.

Sensors (Basel, Switzerland)·2026

Same author

Pathogen-induced modulation of the volatile metabolome of Hanseniaspora uvarum LE-1 and its role in post-harvest disease suppression.

World journal of microbiology & biotechnology·2026

Same author

α-C-H Functionalization of Benzylamines: A Crucial Yet Challenging Transformation.

Chemistry (Weinheim an der Bergstrasse, Germany)·2026

Same author

LiDAR-Based Road Surface Damage Classification: A Survey.

Sensors (Basel, Switzerland)·2026

Same author

Medical student's attitude towards learning medical ethics in India.

Bioinformation·2026

Same author

Security of ADS-B and Remote ID Systems: Cyberattacks, Detection Techniques, and Countermeasures.

Sensors (Basel, Switzerland)·2026

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jun 23, 2025

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

Autonomous Image-Based Corrosion Detection in Steel Structures Using Deep Learning.

Amrita Das¹, Sattar Dorafshan¹, Naima Kaabouch²

¹Department of Civil Engineering, College of Engineering & Mines, University of North Dakota, Grand Forks, ND 58202, USA.

Sensors (Basel, Switzerland)

|June 19, 2024

Summary

This study automated steel corrosion detection using neural networks. EfficientNetB7 achieved 98.5% accuracy, outperforming other models in identifying corroded pixels for structural integrity.

Keywords:

artificial intelligence corrosion semantic segmentation steel structure transfer learning

More Related Videos

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Published on: May 6, 2019

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures

Published on: August 31, 2017

Related Experiment Videos

Last Updated: Jun 23, 2025

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Applicability Analysis of Assessment Methods for Morphological Parameters of Corroded Steel Bars

Published on: November 1, 2018

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Imaging Corrosion at the Metal-Paint Interface Using Time-of-Flight Secondary Ion Mass Spectrometry

Published on: May 6, 2019

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures

Experimental Protocol to Determine the Chloride Threshold Value for Corrosion in Samples Taken from Reinforced Concrete Structures

Published on: August 31, 2017

Area of Science:

Materials Science
Computer Vision
Structural Engineering

Background:

Steel structures face environmental corrosion, posing risks of structural failure.
Current non-destructive testing methods require manual inspection and can miss hidden defects.
Autonomous corrosion detection is crucial for proactive maintenance and safety.

Purpose of the Study:

To evaluate different encoder-decoder neural network architectures for automated corrosion segmentation.
To determine the optimal training strategy for accurate detection of corroded pixels in steel structures.
To compare the performance of various deep learning models in identifying corrosion from visual images.

Main Methods:

Investigated encoder-decoder neural networks with DenseNet121, EfficientNetB7, ResNet34, and UNet backbones.
Compared transfer learning versus full training strategies on segmented corroded pixels.
Evaluated model performance using pixel-level accuracy, true-positive values, and false-positive rates on diverse datasets.

Main Results:

EfficientNetB7 achieved the highest average pixel-level accuracy at 98.5%.
ResNet34 demonstrated superior performance over the original UNet, especially on external datasets with varied corrosion types and colors.
Transfer learning proved more effective than full training for deep networks, particularly with smaller datasets.

Conclusions:

Deep learning models, particularly EfficientNetB7 and ResNet34, show significant promise for autonomous steel corrosion detection.
ResNet34 offers a robust and accurate solution for identifying corrosion in diverse real-world scenarios.
Optimized training strategies and network architectures are key to enhancing the reliability of automated structural health monitoring.