Crack Detection and Classification for Reinforced Concrete Structures using Deep Learning

Abstract

Building cracks such as gaping cracks, separation, and horizontal cracks are a few types of cracks that possess a severe issue on reinforced concrete; hence, the earlier the detection the cheaper the repairs. Numerous studies about crack detection considered VGG16, and Faster R-CNN because the severity level is crucial to each construction company business owner. This paper aims to build a deep learning model using Yolov3 that can detect a crack in reinforce concrete structures and categorize a medium, severe, or very severe crack using an android application. An android application was developed instead of using an expensive Ultrasonic Pulse Velocity in the market to detect the severity of the crack on the concrete. The overall accuracy summary of the android application is 93.33%, while the kappa value is. 97. Therefore, the deep learning model and android application produced an accurate calculation in detecting the crack and determining its crack classification.