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Automated crack detection and mapping of bridge decks using deep learning and drones

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Abstract

Bridge inspection is a crucial process for ensuring the safety and reliability of transportation infrastructure. Traditional bridge inspections are time-consuming, costly, and often require bridges to be closed, disrupting traffic. In recent years, the use of drones and computer vision techniques for bridge inspection has gained attention due to their ability to provide accurate and comprehensive data while reducing costs and disruptions. This paper presents an automated bridge inspection framework that utilizes drones and computer vision techniques for detecting and analyzing cracks on bridge decks. The framework comprises three main components: orthomosaic map generation, deep learning-based crack detection, and georeferencing and visualization in a geographic information system (GIS) platform. The cracks are segmented, identified, and extracted with their georeferenced coordinates, which can be seamlessly integrated into a GIS platform. This integration enables enhanced visualization and spatial analysis of the cracks. In addition, an image data set has been created to facilitate the process of crack segmentation in the context of the proposed automated bridge inspection framework. The network achieved a mIoU of 80.5%, a dice coefficient of 88.1%, a precision of 77.5%, and a recall of 76.5%, highlighting the robust performance of the network in crack detection. The proposed framework was evaluated on a real bridge, and the results showed that it detected and analyzed cracks accurately and efficiently. This framework can be adaptable to various types of infrastructure, making it a valuable tool for managing transportation infrastructure.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

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Acknowledgements

This research was funded in part by Kennesaw State University (KSU). The authors gratefully acknowledge the support KSU. Any opinions, findings, recommendations, and conclusions in this paper are those of the authors, and do not necessarily reflect the views of Kennesaw State University.

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  1. Department of Civil Engineering, Kennesaw State University, 655 Arntson Drive, Marietta, GA, USA

    Da Hu, Tien Yee & Dale Goff

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  1. Da Hu
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Correspondence to Da Hu.

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Hu, D., Yee, T. & Goff, D. Automated crack detection and mapping of bridge decks using deep learning and drones. J Civil Struct Health Monit 14, 729–743 (2024). https://doi.org/10.1007/s13349-023-00750-0

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