Surface crack detection using deep learning with shallow CNN architecture for enhanced computation

Surface cracks on the concrete structures are a key indicator of structural safety and degradation. To ensure the structural health and reliability of the buildings, frequent structure inspection and monitoring for surface cracks is important. Surface inspection conducted by humans is time-consuming and may produce inconsistent results due to the inspectors’ varied empirical knowledge. In the field of structural health monitoring, visual inspection of surface cracks on civil structures using deep learning algorithms has gained considerable attention. However, these vision-based techniques require high-quality images as inputs and depend on high computational power for image classification. Thus, in this study, shallow convolutional neural network (CNN)-based architecture for surface concrete crack detection is proposed. LeNet-5, a well-known CNN architecture, is optimized and trained for image classification using 40,000 images in the Middle East Technical University (METU) dataset. To achieve maximum accuracy for crack detection with minimum computation, the hyperparameters of the proposed model were optimized. The proposed model enables the employment of deep learning algorithms using low-power computational devices for a hassle-free monitoring of civil structures. The performance of the proposed model is compared with those of various pretrained deep learning models, such as VGG16, Inception, and ResNet. The proposed shallow CNN architecture was found to achieve a maximum accuracy of 99.8% in the minimum computation. Better hyperparameter optimization in CNN architecture results in higher accuracy even with a shallow layer stack for enhanced computation. The evaluation results confirm the incorporation of the proposed method with autonomous devices, such as unmanned aerial vehicle, for real-time inspection of surface crack with minimum computation.