%0 Journal Article
%A GUO Wen-ming
%A LIU Kai
%A QU Hui-fan
%T Welding Defect Detection of X-Ray Images Based on Faster R-CNN Model
%D 2019
%R 10.13190/j.jbupt.2019-097
%J Journal of Beijing University of Posts and Telecommunications
%P 20-28
%V 42
%N 6
%X Based on faster region-based convolutional neural networks (R-CNN) model, a classical model in the field of object detection is used to achieve welding defect detection of X-ray images. A great number of X-ray images are collected and sorted out into the dataset, called WDXI, including no-defect type and 7 defect types. Firstly, an improved method can be used to extract the welding area effectively according to the average gray value and the average contrast value per unit area. The adaptive histogram equalization is used for image enhancement and double median blur is used for noise reduction after experimental comparison. Finally, the testing on the pre-trained model is expected and acceptable in the multi-classification problem of welding defects recognition, and not only proves the research value of WDXI, but also contributes towards making an experiment attempt for improving automatic classification and localization of welding defects combined with Faster R-CNN model.
%U https://journal.bupt.edu.cn/EN/10.13190/j.jbupt.2019-097