Real-time crack assessment using deep neural networks with wall-climbing unmanned aerial system

Crack information provides important evidence of structural degradation and safety in civil structures. Existing inspection methods are inefficient and difficult to rapidly deploy. A real-time crack inspection method is proposed in this study to address this difficulty. Within this method, a wall-climbing unmanned aerial system (UAS) is developed to acquire detailed crack images without distortion, then a wireless data transmission method is applied to fulfill real-time detection requirements, allowing smartphones to receive real-time video taken from the UAS. Next, an image data set including 1,330 crack images taken by the wall-climbing UAS is established and used for training a deep-learning model. For increasing detection speed, state-of-the-art convolutional neural networks (CNNs) are compared and employed to train the crack detector; the selected model is transplanted into an android application so that the detection of cracks can be undertaken on a smartphone in real time. Following this, images with cracks are separated and crack width is calculated using an image processing method. The proposed method is then applied to a building where crack information is acquired and calculated accurately with high efficiency, thus verifying the practicability of the proposed method and system.