Defect Detection Algorithm Based on Hd-PHC Feature Screening.

Defect detection is one of the important research directions in the field of machine vision, and positive sample learning is one of the important algorithms. By focusing on the reconstruction of background region, positive sample learning has been widely used in many occasions. However, in the process of use, a large number of defect-free samples are often required as training data. Obviously, some application situations cannot meet such requirements. Therefore, this paper proposes a preprocessing algorithm based on the Hamming distance of Patch Histogram Coding feature screening algorithm (Hd-PHC) and neighborhood structure. By segmentation and screening of defective images, defect-free data can be obtained for training. In this way, the source problem of training data can be solved, and the network scale can be optimized to some extent to alleviate the problems caused by the differences between different backgrounds. On the simulation data set and the actual data set generated by AITEX data set, the Hd-PHC feature screening algorithm reached 99.67% accuracy, the screening rate reached 64.31%, and the average time was 120.27s. The simulation data set and the actual data set reached 99.96% and 99.93% accuracy, respectively, F1 was 0.8919 and 0.8225.