Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets

TL;DR

This paper reviews recent advances in deep learning for crack detection, highlighting paradigm shifts, generalizability improvements, and dataset diversification, and introduces a new 3D laser scan dataset with benchmarking results.

Contribution

It systematically analyzes emerging trends in deep learning-based crack detection and introduces a novel 3D laser scan dataset with benchmarking for future research.

Findings

Transition from supervised to semi-supervised and unsupervised learning paradigms.

Enhanced cross-dataset generalizability in crack detection models.

Introduction of 3DCrack, a new 3D laser scan dataset for crack detection.

Abstract

Crack detection plays a crucial role in civil infrastructures, including inspection of pavements, buildings, etc., and deep learning has significantly advanced this field in recent years. While numerous technical and review papers exist in this domain, emerging trends are reshaping the landscape. These shifts include transitions in learning paradigms (from fully supervised learning to semi-supervised, weakly-supervised, unsupervised, few-shot, domain adaptation and fine-tuning foundation models), improvements in generalizability (from single-dataset performance to cross-dataset evaluation), and diversification in dataset acquisition (from RGB images to specialized sensor-based data). In this review, we systematically analyze these trends and highlight representative works. Additionally, we introduce a new annotated dataset collected with 3D laser scans, 3DCrack, to support future research and conduct extensive benchmarking experiments to establish baselines for commonly used deep learning methodologies, including recent foundation models. Our findings provide insights into the evolving methodologies and future directions in deep learning-based crack detection. Project page: https://github.com/nantonzhang/Awesome-Crack-Detection