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<p>This paper presents a novel artificial intelligence (AI) and Internet of Things (IoT) framework for structural health monitoring (SHM) of masonry bridges. The system utilises the Single Shot MultiBox Detector (SSD) MobileNetV2 model within the TensorFlow Object Detection API to automatically detect critical defects such as spalling, section loss, missing masonry units, and open joints. The model achieved a mean Average Precision (mAP) of 87.4% and an F1-score of 0.89, demonstrating its reliable performance in classifying and localising defects. Through detailed analysis using TensorBoard, the study demonstrates the reliable performance of the model in classifying and localising defects, enabling timely maintenance interventions. By automating defect detection and data analysis, this approach improves monitoring efficiency, reduces operational costs, and improves safety compared to traditional manual inspections. The paper also discusses the potential for future optimisation and real-world deployment to support sustainable management of masonry bridge infrastructure.</p>

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Application of SSD-MobileNetV2 for automated defect detection in masonry bridges using AI and IoT

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月20日

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  • Ebenezer Amoako,
  • Yong Sheng,
  • Muhammad Khalid,
  • Marina Bock

摘要

This paper presents a novel artificial intelligence (AI) and Internet of Things (IoT) framework for structural health monitoring (SHM) of masonry bridges. The system utilises the Single Shot MultiBox Detector (SSD) MobileNetV2 model within the TensorFlow Object Detection API to automatically detect critical defects such as spalling, section loss, missing masonry units, and open joints. The model achieved a mean Average Precision (mAP) of 87.4% and an F1-score of 0.89, demonstrating its reliable performance in classifying and localising defects. Through detailed analysis using TensorBoard, the study demonstrates the reliable performance of the model in classifying and localising defects, enabling timely maintenance interventions. By automating defect detection and data analysis, this approach improves monitoring efficiency, reduces operational costs, and improves safety compared to traditional manual inspections. The paper also discusses the potential for future optimisation and real-world deployment to support sustainable management of masonry bridge infrastructure.