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Corrosion of Reinforcement

Corrosion of Reinforcement

The corrosion of steel reinforcement within concrete is a process influenced by the material's inherent properties and external factors. The high pH level of around 13, provided by calcium hydroxide present in concrete, initially protects the steel reinforcement by promoting the formation of a passive iron oxide layer on its surface.
However, over time and under certain conditions like carbonation, chloride ingress, and cracking this protective state can be compromised. Steel has areas with...

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Updated: Apr 19, 2026

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

Prototype-Based Multi-Dimension Intensity Mapping Density Sampling Network for Corrosion Segmentation.

Xinyu Chen, Bohao Zhao, Gaoyang Pang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|April 17, 2026

Summary

This summary is machine-generated.

This study introduces the Prototype-based Multi-dimension Sample-Adaptive Intensity Mapping with Density Sampling (PMSAD) network for advanced corrosion semantic segmentation (CSS). PMSAD significantly improves the accurate detection and precise boundary delineation of corrosion in complex environments.

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Last Updated: Apr 19, 2026

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Published on: September 4, 2016

Area of Science:

Engineering
Computer Science
Materials Science

Background:

Corrosion semantic segmentation (CSS) is crucial for early detection and positioning of corrosion.
Diverse corrosion forms, blurred boundaries, and intra-class heterogeneity present significant challenges in CSS.

Purpose of the Study:

To propose a novel network, PMSAD, to address the challenges in CSS.
To enhance intra-class cohesion and inter-class separation for diverse corrosion patterns.
To improve feature discrimination and robustness against illumination variations.

Main Methods:

Developed a Prototype-based Multi-dimension Sample-Adaptive Intensity Mapping with Density Sampling (PMSAD) network.
Incorporated nonparametric nearest prototype retrieving for class separation.
Designed Multi-Scale Dual Attention (MSDA), Multi-dimension Sample-adaptive Intensity Mapping (MSAIM), and Density Sampling (DS) modules.
Utilized adaptive RGB channel intensity adjustment and density-focused training refinement.

Main Results:

PMSAD achieved superior performance and generalization ability on real-world datasets.
The network demonstrated state-of-the-art precise boundary delineation and accurate corrosion classification.
Effectively handled diverse corrosion forms, blurred boundaries, and intra-class variations.

Conclusions:

PMSAD offers a robust solution for corrosion semantic segmentation.
The proposed methods effectively tackle the complexities of real-life corrosion patterns.
Achieved new state-of-the-art results in precise corrosion detection and classification.