Feature extraction based on Gabor filter and Support Vector Machine classifier in defect analysis of Thermoelectric Cooler Component

Abstract Purpose During the slicing process, thermoelectric cooler (TEC) component are prone to cause various defects, such as cracks, pockmarks, and other defects of various shapes and sizes. The presence of these surface defects will lead to a decline in product performance. At present, the method of screening TEC component is mainly manual detection. This method is affected by the strong subjectivity of human beings and the limited spatial resolution. Therefore, a method based on machine learning is proposed to overcome the above shortcomings and obtain product accuracy that is difficult for artificial vision to achieve Method We use Gabor filters to extract texture information in the TEC component image and use it as a classification feature. At the same time, principal component analysis (PCA) is used to select the classification features. For the number of defect types, we use support vector machine (SVM) to classify unknown defects. By extracting the features of the TEC component image and using it for SVM training. Finally, the unknown defect image is used as input to obtain the defect category Results By testing the TEC simultaneously with other machine learning methods in this paper, the superiority of this method is proved. At the same time, it also overcomes a series of problems such as slow manual detection speed, low reliability, poor consistency and stability of test results, product quality cannot be improved, and low detection efficiency Conclusion Based on our experimental analysis, the proposed method can effectively improve the classification effect of TEC component defects. Compared with other classifiers, the effectiveness of this method is further verified.