关键词: X射线图像, 独立分量分析, 缺陷检测, 机器学习, 厚钢管

Abstract: Due to the intensity distribution of X-ray image of thick steel pipe is not uniform, the contrast is low, the noise is big, and the size, shape, position and contrast of the blowholes defects are different, it is difficult to detect various types of blowholes automatically. Aiming at the problems that the traditional defect detection algorithm has a large workload of manually marking defect data, and the edge of the weld is difficult to accurately extract and other issues, a new unsupervised learning algorithm was proposed for the detection of various blowholes defects. Firstly, fast Independent Component Analysis (ICA) was used to learn a set of independent base vectors from the steel pipe X-ray image set, and a linear combination of the base vectors was used to selectively reconstruct the test image with blowholes defect. Then, the test image was subtracted from its reconstructed image to obtain the difference image, and the various blowholes were separated from the difference image by global threshold. There were 320 images in the training set and 60 images in the test set. The average sensitivity and accuracy of the proposed algorithm were 90.5% and 99.7%. The experimental results show that the algorithm can accurately detect all kinds of blowholes defects without manual marking the data or extracting the edge of the weld.

Key words: X-ray image, Independent Component Analysis (ICA), defect detection, machine learning, thick steel pipe