Transformer-Based Intelligent Defect Detection in Civil Infrastructure

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Detecting structural and functional defects in large-scale civil infrastructure during operational stages is critical for ensuring safety, serviceability, and sustainability. Traditional inspection methods are often labor-intensive, subjective, and inefficient for large-scale systems. With the rapid evolution of artificial intelligence, deep learning has emerged as a powerful tool for intelligent defect detection. Recently, Transformer-based self-attention models have gained prominence as effective alternatives to convolutional neural networks (CNNs), offering superior capability in modeling long-range dependencies and parallel computation. This research-oriented survey systematically explores the integration of Transformer architectures into civil engineering defect detection applications.

Evolution from CNNs to Transformer Models

Conventional CNN-based approaches have demonstrated strong performance in localized feature extraction for defect detection tasks; however, their limited receptive fields restrict the modeling of global structural relationships. Transformers overcome these limitations through self-attention mechanisms that capture global contextual information across large-scale datasets. This paradigm shift has motivated civil engineering researchers to adopt Transformer models for infrastructure monitoring, enabling more comprehensive understanding of spatial and temporal defect patterns.

Transformer Architectures for Engineering Defect Detection

This survey reviews more than 40 Transformer-based engineering defect detection algorithms, highlighting key architectural variants such as Vision Transformers (ViT), hybrid CNN-Transformer models, and hierarchical attention frameworks. These architectures are tailored to handle high-resolution images, sensor data, and multimodal inputs commonly encountered in civil infrastructure monitoring. The adaptability of Transformer models allows effective feature learning across complex structural geometries and diverse defect manifestations.

Application Scenarios in Civil Infrastructure

Transformer-based defect detection methods have been extensively applied to critical civil infrastructure systems, particularly roadways, tunnels, and bridges. In roadway monitoring, Transformers enable accurate detection of cracks, potholes, and surface degradation. Tunnel inspection applications benefit from long-range dependency modeling in low-light and complex environments, while bridge monitoring leverages attention mechanisms to identify structural anomalies across large spans and interconnected components.

Challenges and Limitations

Despite their advantages, Transformer-based approaches face several challenges in civil engineering applications. These include high computational costs, large data requirements, limited labeled datasets, and difficulties in real-time deployment. Additionally, domain adaptation across different infrastructure types and environmental conditions remains a significant research challenge. Addressing these issues is essential for practical, scalable adoption in real-world infrastructure systems.

Future Research Directions

Future development of Transformer-based intelligent detection systems should focus on lightweight architectures, self-supervised learning, multimodal data fusion, and domain-specific model optimization. Integrating Transformers with digital twins, edge computing, and real-time monitoring systems holds promise for next-generation smart infrastructure management. This survey provides foundational insights and reference strategies to guide researchers in advancing intelligent defect detection within civil engineering.

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#RoadwayEngineering
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#IntelligentSystems
#DigitalInfrastructure
#EngineeringResearch
#StructuralSafety
#AutomationInCivil
#FutureEngineering
#SustainableInfrastructure


 

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