Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review

doi:10.1109/TITS.2024.3365296

Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review

2024-09-01

3

PRE

AI

Jean‐François Lapointe

DOI：10.1109/TITS.2024.3365296

Original

Original request for help

Save

Abstract

En 中文

Visual inspection is an important process for maintaining bridges in road transportation systems, and preventing catastrophic events and tragedies. In this process, accurate and automatic concrete defect classification and detection are major components to ensure early identification of any issue that can compromise the bridge safety and integrity. While a tremendous body of research has been proposed in the last decades for addressing these problems, the advent of deep learning unleashed huge opportunities for building more accurate and efficient methods. Our aim in this survey is to study the recent progress of vision-based concrete bridge defect classification and detection in the deep learning era. Our review encompasses major aspects underlying typical frameworks, which include concrete defect taxonomy, public datasets and evaluation metrics. We provide also a taxonomy of deep-learning-based classification and detection algorithms with a detailed discussion of their advantages and limitations. We also benchmark baseline models for classification and detection, using two popular datasets. We finally discuss important challenges of concrete defect classification and detection, and promising research avenues to build better models and integrate them in real-world visual inspection systems, which warrant further scientific investigation.

Keywords:

Bridges

Concrete

Inspection

Visualization

Reviews

Deep learning

Annotations

Concrete bridge defects

classification

detection

segmentation

deep learning

Visual Concrete Bridge Defect Classification and Detection Using Deep Learning: A Systematic Review

Abstract

Journal

Organization