Vehicular Road Crack Detection with Deep Learning: A New Online Benchmark for Comprehensive Evaluation of Existing Algorithms

2025-03-23

AI Summary

Current road crack detection research lacks systematic evaluation of data fusion and label-efficient algorithms, and no online benchmark jointly assesses detection accuracy, computational efficiency, and cross-domain generalization. Method: We introduce UDTIRI-Crack—the first large-scale, vehicle-mounted online benchmark (2,500 high-quality images)—and conduct the first unified evaluation of supervised, unsupervised, semi-supervised, and weakly supervised label-efficient methods. We further pioneer the adaptation of multimodal foundation models and large language models to crack detection. Leveraging CNN/Transformer backbones, our framework integrates multi-source data fusion, prompt-based fine-tuning, and generalization analysis to comprehensively evaluate over 20 state-of-the-art algorithms. Contribution/Results: Our empirical study reveals fundamental trade-offs among accuracy, inference speed, and robustness across domains. The fully open-sourced, reproducible platform establishes a standardized testbed to accelerate algorithmic innovation and iterative improvement in intelligent infrastructure inspection.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Multi-instance/Multi-view Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, there is a lack of systematic reviews on state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. Also, we create a dataset called UDTIRI-Crack, comprising $2,500$ high-quality images from seven public annotated sources, as the first extensive online benchmark in this field. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. In addition, the feasibility of foundation models and large language models (LLMs) for road crack detection is explored. Afterwards, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The released benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.

Problem

• Lack of systematic reviews on deep learning for road crack detection.
• Need for comprehensive benchmark to evaluate existing crack detection algorithms.
• Exploring foundation models for improving road crack detection accuracy.

Innovation

• Comprehensive review of deep learning crack detection methods
• Creation of UDTIRI-Crack benchmark dataset
• Exploration of foundation models for crack detection