Feature extraction of wood-hole defects using wavelet-based ultrasonic testing

The primary bottleneck to extracting wood defects during ultrasonic testing is the accuracy of identifying the wood defects. The wavelet energy moment was used to extract defect features of artificial wood holes drilled into 120 elm samples that differed in the number of holes to verify the validity of the method. Wavelet energy moment can reflect the distribution of energy along the time axis and the amount of energy in each frequency band,which can effectively extract the energy distribution characteristics of signals in each frequency band; therefore,wavelet energy moment can replace the wavelet frequency band energy and constitute wood defect feature vectors. A principal component analysis was used to normalize and reduce the dimension of the feature vectors. A total of 16 principal component features were then obtained, which can effectively extract the defect features of the different number of holes in the elm samples.

Printed Surface Defect Detection Model Based on Positive Samples

For a long time, the detection and extraction of printed surfacedefects has been a hot issue in the print industry. Nowadays, defect detectionof a large number of products still relies on traditional image processingalgorithms such as scale invariant feature transform (SIFT) and orientedfast and rotated brief (ORB), and researchers need to design algorithms forspecific products. At present, a large number of defect detection algorithmsbased on object detection have been applied but need lots of labeling sampleswith defects. Besides, there are many kinds of defects in printed surface,so it is difficult to enumerate all defects. Most defect detection based onunsupervised learning of positive samples use generative adversarial networks(GAN) and variational auto-encoders (VAE) algorithms, but these methodsare not effective for complex printed surface. Aiming at these problems, Inthis paper, an unsupervised defect detection and extraction algorithm forprinted surface based on positive samples in the complex printed surface isproposed innovatively. We propose a kind of defect detection and extractionnetwork based on image matching network. This network is divided into thefull convolution network of feature points extraction, and the graph attentionnetwork using self attention and cross attention. Though the key pointsextraction network, we can get robustness key points in the complex printedimages, and the graph network can solve the problem of the deviation becauseof different camera positions and the influence of defect in the differentproduction lines. Just one positive sample image is needed as the benchmarkto detect the defects. The algorithm in this paper has been proved in “TheFirst ZhengTu Cup on Campus Machine Vision AI Competition” and gotexcellent results in the finals. We are working with the company to apply it inproduction.