Deep Learning Framework for Infrastructure Maintenance: Crack Detection and High-Resolution Imaging of Infrastructure Surfaces

Recently, there has been an impetus for the application of cutting-edge data collection platforms such as drones mounted with camera sensors for infrastructure asset management. However, the sensor characteristics, proximity to the structure, hard-to-reach access, and environmental conditions often limit the resolution of the datasets. A few studies used super-resolution techniques to address the problem of low-resolution images. Nevertheless, these techniques were observed to increase computational cost and false alarms of distress detection due to the consideration of all the infrastructure images i.e., positive and negative distress classes. In order to address the pre-processing of false alarm and achieve efficient super-resolution, this study developed a framework consisting of convolutional neural network (CNN) and efficient sub-pixel convolutional neural network (ESPCNN). CNN accurately classified both the classes. ESPCNN, which is the lightweight super-resolution technique, generated high-resolution infrastructure image of positive distress obtained from CNN. The ESPCNN outperformed bicubic interpolation in all the evaluation metrics for super-resolution. Based on the performance metrics, the combination of CNN and ESPCNN was observed to be effective in preprocessing the infrastructure images with negative distress, reducing the computational cost and false alarms in the next step of super-resolution. The visual inspection showed that EPSCNN is able to capture crack propagation, complex geometry of even minor cracks. The proposed framework is expected to help the highway agencies in accurately performing distress detection and assist in efficient asset management practices.