综述裂缝检测的深度学习新范式与数据集进展
Deep Learning for Crack Detection: A Review of Learning Paradigms, Generalizability, and Datasets
- 梳理从监督学习到自适应、少样本等新学习范式
- 提出3D激光扫描数据集3DCrack，支持跨数据集评估
- 评测主流模型，为未来研究提供基准参考
裂缝检测在道路、建筑等土木基础设施巡检中至关重要，近年来深度学习显著推动了该领域发展。现有技术与综述论文众多，但新兴趋势正重塑研究格局：学习范式从全监督转向半监督、弱监督、无监督、少样本学习、域自适应及微调基础模型；泛化能力要求从单一数据集性能扩展至跨数据集评估；数据采集方式也由普通RGB图像拓展至专用传感器数据。本文系统分析上述趋势，总结代表性工作，并引入一个通过3D激光扫描构建的新标注数据集3DCrack，以支持未来研究。同时，开展广泛的基准测试，建立常用深度学习方法（包括近期基础模型）的性能基准。研究成果为基于深度学习的裂缝检测方法演进与未来方向提供了重要洞察。
原文摘要 · Abstract (English)
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
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