What type of study is this?

This is a Experimental Study study.

October 17, 2025

AI for Automatic Defects Detection on Large Civil Works: Our Adventure from the Database Creation to a First Efficient Pipeline

Puntos clave

Automated analysis significantly reduces the time and cost involved in identifying structural defects.
The project created a unique training database and employed neural networks for effective defect detection.
State-of-the-art image processing techniques were utilized, including data augmentation for improved model performance.
Initial results show promising feedback on the efficiency of the automated pipeline for defect analysis.

Resumen

EDF, as a responsible operator, inspects and maintains the civil engineering structures of its nuclear power plants, which often exceed several tens of thousands of square meters in surface area. These inspections are carried out using drones, generating orthophotos of the surfaces. Manual analysis of these images to identify defects such as cracks, corroded bars, and spalling is costly and time-consuming. To address these issues, EDF is developing an AI-based tool for automated analysis to detect, locate, and measure these defects. The project includes a state-of-the-art review, creation of a training database, image preprocessing, data augmentation, neural network implementation, performance metrics selection, and optimization of the processing chain. The first results obtained and feedback on this image processing pipeline will be presented.

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