Enhancing crack detection and severity assessment in historical Tabiya basins using U-Net and adaptive thresholding
Hafsa Matich, Jamal Attmani, Hajar Mousannif
TL;DR
This paper presents an AI-based system for detecting and measuring cracks in historical Tabiya basins, improving conservation efforts through automated analysis.
Contribution
The novel contribution is an automated crack detection and severity assessment system using U-Net and adaptive thresholding for heritage infrastructure.
Findings
The MobileNetV2-based model achieved 98.7% accuracy in crack detection.
The system includes quantitative measurements like crack length, width, and severity assessment.
A web application was developed for user-friendly automated analysis of basin images.
Abstract
The conservation of the historical Tabiya water basins remains paramount, with consideration being their cultural and architectural importance, though structural degeneration like surface cracking poses a formidable challenge to conservation work. Since the traditional methods of inspection are often subjective, tedious, and prone to error, these limitations are tackled in this study by means of presenting an automated system for surface crack detection and segmentation based on artificial intelligence and computer vision techniques. High-resolution images were captured on-site using a Canon EOS 1100D camera and analyzed within a comparative deep learning framework using four models, namely U-Net with MobileNetV2, ResNet-50, InceptionV3, and EfficientNetB7 backbones. The proposed system performs crack detection and segmentation, as well as quantitative measurements, including crack…
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Taxonomy
TopicsInfrastructure Maintenance and Monitoring · 3D Surveying and Cultural Heritage · Water Systems and Optimization