Structural Health Monitoring Using Drone Technology

Abstract

This project introduces a wall crack detection system that uses computer vision and image processing techniques to identify and classify cracks. The system accepts an image or video input and analyzes it to detect crack types such as shrinkage, minor, major, and structural cracks. It calculates the length and area of each crack and creates a detailed health report of the wall. The system is designed to help civil engineers determine structural safety by automaticallydetecting faults. The final report includes visual findings, crack measurements, and a general evaluation of wall condition.

Introduction

This project presents a low-cost, automated wall crack detection system using rule-based image processing techniques, aimed at improving the efficiency and accuracy of structural inspections. Traditional manual inspections are labor-intensive and inconsistent, especially for large structures.

System Overview

• Input: Wall images/videos from a camera or mobile device.

• Processing: Classic computer vision methods (grayscale conversion, Gaussian blur, Canny edge detection, morphological operations, and contour analysis) detect and measure cracks.

• Output: A report detailing crack types, dimensions, and structural health status.

• Interface: Built with Gradio for ease of use by non-technical users.

Key Features

• Real-time crack detection without deep learning (initial phase).

• Measures crack length and area using OpenCV’s cv2.arcLength().

Literature Insights

• Drones and AI/ML (e.g., YOLOv7, Faster R-CNN, DetectorX) enhance structural health monitoring (SHM).

• NDT techniques and IoT integration offer safer, real-time structural evaluations.

• Case studies show AI-powered drones can achieve up to 98.7% accuracy in detecting flaws.

Methodology & Future Scope

• Built using Python, OpenCV, and Gradio.

• Modular and scalable for future integration with machine learning models.

• Aims to expand to large infrastructure (bridges, tunnels) and support residential and industrial maintenance.

Conclusion

We can conclude here that the integration of drone technology with Structural Health Monitoring (SHM) systems constitutes a revolutionizing step in the evaluation and maintenance of vital infrastructure. The merger of cutting-edge aerial technology with advanced analytical techniques represents a groundbreaking shift, as emphasized by various literature and on-going study. Unprecedented efficiency and remote access to spaces long difficult or considered risky are provided by drones. Their capacity to carry out detailed inspections without compromising human safety is a foundation for timely evaluation of infrastructure integrity. With the integration of high-resolution imaging sensors and the application of sophisticated image processing methods, drones facilitate the acquisition of detailed data, setting the stage for strong analysis. Automated defect detection and classification algorithms greatly assist in the detection of structural anomalies, offering a vehicle for proactive and precise assessment. In addition, the use of non-destructive techniques highlighted in a number of research works identifies the ability of drones to carry out remote, non-contact inspections. This feature is necessary for periodic, damage-free inspection of structures to enable maintenance without destruction. The automation of defect detection, classification, and thresholding processes is important in order to accelerate the detection of structural anomalies. These automated processes, combined with drone technology, simplify inspection and analysis, especially for mass infrastructure.

References

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Copyright

Copyright © 2025 Ankush Tiwari, Ankit Tejam, Dr. Lochan Jolly, Arpit S. Vyas. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.