Automated detection and segmentation of internal defects in reinforced concrete using deep learning on ultrasonic images

Abstract

Periodic inspection of concrete structures for detecting internal defects through nondestructive imaging is recommended for ensuring structural safety and integrity. Ultrasonic imaging is one of the emerging and widely accepted techniques in nondestructive diagnostics of concrete structures. However, manually screening the ultrasonic images to detect and localize anomalies over large areas especially those from public infrastructure is tedious and prone to misjudgments. This work proposes a Region-based CNN to automatically detect, localize, and segment those signatures corresponding to defects from the noisy ultrasonic images with multiple features. This network replaces the conventional RoI pooling adopted in RCNNs with RoIAlign. To optimize the model for real-life complexity and noise levels, the network is trained on real experimental data. The ultrasonic image dataset has been generated by applying the imaging technique based on the Synthetic Aperture Focusing Technique on reflected waveform data obtained on various specimens with installed defects. The proposed network has been trained on the labelled image dataset containing signatures corresponding to multiple flaws like debonded rebars, delaminations, and planar crack-like defects in concrete members. The predictions of the trained model over the test datasets detect and masks the pixels corresponding to defects in ultrasonic images with a best mAP of 0.98. Segmentation results of the developed model upon testing are found superior to other state-of-the-art defect detection networks.