Abstract
With the development of computer vision technology, more and more enterprises begin to use computer vision instead of manual inspection for steel surface defect detection. However, classical image processing methods often face great difficulties when dealing with images containing noise and distortions, which leads to low computational efficiency and poor accuracy of detection. In view of the particularity of hot round steel production, a computational intelligence method is proposed in this paper. On the basis of preliminary image preprocessing, we combine the improved PCA with genetic algorithm for feature selection and then use evolutionary computing and CUDA-based parallel computing to screen out the suspected defective image of round steel surface intelligently, quickly, and accurately. This method can provide decision support for subsequent defect analysis and production process improvement.
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