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

Ebenezer Amoako; Yong Sheng; Muhammad Khalid; Marina Bock

doi:10.1186/s43251-025-00179-z

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

Ebenezer Amoako^*
, Yong Sheng
, Muhammad Khalid
, Marina Bock

^*Corresponding author for this work

University of Hull
Aston University

Research output: Contribution to journal › Article › peer-review

3 Citations (Scopus)

2 Downloads (Pure)

Abstract

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.

Original language	English
Article number	31
Number of pages	41
Journal	Advances in Bridge Engineering
Volume	6
Early online date	20 Sept 2025
DOIs	https://doi.org/10.1186/s43251-025-00179-z https://doi.org/10.1186/s43251-025-00179-z
Publication status	Published - 20 Sept 2025

Bibliographical note

Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.

Keywords

Artificial intelligence
Bridge inspection
Defect detection
Internet of things
Masonry bridges
Object detection
Real-time data analysis
Structural health monitoring

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Application of SSD-MobileNetV2 for automated defect detection in masonry bridges using AI and IoT
Copyright © The Author(s) 2025. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/.
Final published version, 5.55 MBLicence: CC BY 4.0

Cite this

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

abstract = "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.",

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N2 - 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.

AB - 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.

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

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