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.

中文翻译：

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使用深度神经网络和壁挂式无人航空系统进行实时裂缝评估

裂缝信息提供了有关民用建筑结构退化和安全性的重要证据。现有的检查方法效率低下且难以快速部署。为了解决这一难题，本研究提出了一种实时裂纹检查方法。在这种方法中，开发了一种爬壁无人机系统（UAS）以获取无失真的详细裂缝图像，然后应用无线数据传输方法满足实时检测要求，从而使智能手机可以接收从无人机系统。接下来，建立了一个图像数据集，其中包括由爬壁式UAS拍摄的1,330张裂缝图像，并将其用于训练深度学习模型。为了提高检测速度，将比较先进的卷积神经网络（CNN）并将其用于训练裂纹检测器。选定的模型将移植到android应用程序中，以便可以在智能手机上实时进行裂缝检测。随后，分离出带有裂纹的图像，并使用图像处理方法计算出裂纹宽度。然后将所提出的方法应用于高效率地准确获取和计算裂缝信息的建筑物，从而验证了所提出的方法和系统的实用性。