Automatic detection of concrete cracks from images using Adam-SqueezeNet deep learning model

Abstract

Cracks on concrete surface are typically clear warning signs of a potential threat to the integrity and serviceability of structure. The techniques based on image processing can effectively detect the cracks from images. These techniques, however, are generally susceptible to user-driven heuristic thresholds and extraneous distractors. Inspired by recent success of artificial intelligence, a deep learning based automated crack detection system called CrackSN was developed. An image dataset of concrete surface is collected by smartphone and carefully prepared in order to develop and train the CrackSN system. This proposed deep learning model, built on the Adam-SqueezeNet architecture, automatically learns the discriminative feature directly from the labeled and augmented patches. Hyperparameters of SqueezeNet are tuned with Adam optimization additive through the training and validation procedures. The fine-tuned CrackSN model outperforms state-of-the-art models in recent literature by correctly classifying 97.3% of the cracked patches in the image dataset. The success of CrackSN model demonstrated with light network design and outstanding performance provides a key step toward automated damage inspection and health evaluation for infrastructure.

Rapid and Accurate Identification of Concrete Surface Cracks via a Lightweight & Efficient YOLOv3 Algorithm

“…Owing to the large-scale characteristic of transportation infrastructure, the distribution of cracks in concrete materials and structures often exhibits divergence and dispersion [7,8]. The occurrence of cracks may impair the integrity and performance of related infrastructural systems [9,10]. The rapid and accurate detection of cracks in concrete materials and structures becomes exceptionally crucial to the safety of transformation infrastructure [11][12][13].…”

Deep Learning-Based Automated Detection of Cracks in Historical Masonry Structures

“…Deep learning-based automated crack detection systems have been developed for masonry structures [27,28]. These systems utilize deep convolutional neural networks and image-processing techniques to detect cracks on concrete and masonry surfaces [29,30]. The models are trained on labeled and augmented image datasets, achieving high accuracy in crack classification.…”