automatic visual inspection system based on image processing and neural network for quality control of sandwich panel

In this work, an automatic system based on the image processing methods using the features based on convolutional neural networks is proposed in order to detect the degree of possible dipping and buckling on the sandwich panel surface by a colour camera. the proposed method, by receiving an image of the sandwich panel, can detect the dipping and buckling of its surface with an acceptable accuracy. after a panel is fully processed by the system, an image output is generated in order to observe the surface status of the sandwich panel so that the supervisor of the production line can better detect any potential defects at the surface of the produced panels. an accurate solution is also provided in order to measure the amount of available distortion (depth or height of dipping and buckling) on the sandwich panels without the need for expensive and complex equipment and hardware