Bore flaw is the important influencing factor to service life of gun barrel. Traditional manual detection method is tedious and imprecise. To improve the detection efficiency and accuracy, an intelligent bore peek and measurement system based on machine vision technology was developed. And a new kind of flaw image enhancement, image division, feature extraction and identification method is put forward. Curvelet transformation and direct grey mapping are adopted to enhance original image and reduce noise. PSNR results based on experiments verify the denoise effectiveness. Transitional areas of rifles and tail-end area of gas port are taken as example to illustrate the flaw image division principle. To extract the textural features from bore flaw images, grey level co-occurrence triangular matrix (GLCTM) theory is adopted and usual statistic functions are established. Six kinds of typical flaws are taken as experiment objects of texture extraction. The experiment results prove the credibility and accuracy of the proposed method in this paper.
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