Automatic real-time detection of structural damages on site is a required technology to enable rapid, accurate, and on-site inspection. This paper introduces an automated intelligent inspection system capable of detecting structural problems, such as cracks, in real-time at the edge of power plant components. Since no available dataset was suitable for this case study, a real dataset was created by combining new and existing. For inspection, this project customized a Deep Neural Network (DNN) model to fit our application, including its quantization to enable deployment at the edge. Real-time, on-site results from aerial and hand-held setup images of the stack of an old power plant show that the system is capable of identifying and localizing cracks within the field of view (FOV) of the camera with a mean average precision (mAP) of 98.44% and ∼ at 2.5 frames per second (FPS) with real-time inference for crack detection at the edge.