Intelligent Flaw Detection of X-ray Images Based on Deep Learning

X-ray testing is one of the main methods of industrial non-destructive testing, and its testing results have been used as an important basis for defect analysis and quality evaluation. Manual recognition of defects is the traditional method of X-ray testing in the industry. Observers need to keep an eye on the monitor and artificially screen for defects based on experience and industry standards. In the case of working for a long time, the observer’s efficiency will be reduced, and there will be misjudgment or omission, so that the components that do not meet the quality grade will enter the market, resulting in unpredictable losses. Because different observers have different judgments on the test results, the evaluation results are difficult to be unified, which is disadvantageous to the defect detection and identification work. In order to reduce manual intervention flaw detection, this work uses the method based on computer deep learning to intelligently detect flaws in X-ray images. The X-ray data set comes from the American Society for Testing Material (ASTM) standard radiogram, Panyu Chu Kong Steel Pipe (Zhuhai) Co., Ltd. and Gree Electric Appliances, Inc. of Zhuhai, which are divided into the training set and verification set according to the proportion of 70% and 30%. In this work, the models such as FCN, YOLO and Mask R-CNN are used to detect inclusions or bubbles in X-ray images, and it is found that Mask R-CNN model has the highest detection precision and recognition rate, and this model has certain generalization ability.