Abstract:
Advancement of imaging technology and computing resources make crack detection in concrete automated using a
vision-based approach. The present work focuses on crack detection in laboratory-scale concrete cubes used for the characterization of
concrete using the convolutional neural network. The major challenge in the said application is to remove inherent noise and dents from
the uneven surface of the test cube. A laboratory-scale image acquisition setup was developed to acquire consistent images of concrete
cubes. Inceptionv3 architecture was trained to detect the cracks in concrete cube surface images in the most accurate manner. The
Inceptionv3 model was trained and validated using more than 80,000 crack and 80,000 non-crack images dataset prepared manually
using the concrete cube surface images. Popular data augmentation techniques were used to generate the training dataset. An average of
97.49% accuracy and 7.38% cross-entropy are achieved in the training whereas 97.67% accuracy and 7.69% cross-entropy are achieved
in the model validation. The training was carried out with a batch size of 100 and 5,000 epochs. An average accuracy of 99% has
been achieved during the performance evaluation of crack detection on concrete cubes as presented in the results. The average values
of precision, recall and F – score are obtained as 0.88, 0.98 and 0.93 respectively.
