An adaptive selection of filter parameters: Defect detection in steel image using wavelet reconstruction method

We proposed a scheme for adaptively selecting filter parameters for detecting defects in various image textures. To implement the proposed scheme on a target steel image, we used wavelet reconstruction method. The adaptive parameter-selecting scheme was presented by analyzing the textures in an image and obtaining the appropriate parameters from a pretrained neural network by inputting these texture features. Experiments were conducted to detect corner cracks in the images of a steel billet, and the proposed scheme was compared with a conventional wavelet reconstruction method. The experimental results showed that our proposed scheme was effective in detecting defects in various textures of the target images.