Key technology of detecting hot heavy rail steel surface faults based on machine vision

Aiming at the low efficiency and precision of hot rail steel surface faults detecting at present,a suit of surface defect detection system of hot heavy rail based on the machine vision is put forward. Multi-CCD cameras are used to collect pictures. According to the geometric characteristics of the heavy rail and its defect characteristics of high-frequency region,six angle shot is used for heavy rail,and then various image processing technology are adopted in workstation. The system adopts improved Hough transform to get surface faults and Kohonen network to make a classification for the characteristics of low SVM training algorithm. The above key machine vision technology for detection of hot heavy rail surface defects greatly improves the speed and accuracy of testing and the detecting correct rate arrives over 85%.     

The image definition improved algorithm for hot continuous castingbillet surface defects inspection

In the hot continuous casting billet surface defect inspection system based on machine vision, acquiring a high signal-to-noise image is the key for successful inspection. To solve the disadvantages existing in current machine vision engineering, a new algorithm with improved image definition is presented based on both focus window and CCD target area illumination parameters. It selects a target object from series of hot continuous casting billet surface images, and then acquires the optimum articulation through focus window square gradient algorithm. By recognizing and calculating target’s area loss rate, target area parameter evaluation can be done. The global optimum image quality point is achieved. The algorithm is effective in selecting focus plane and shutter time during hot continuous casting surface imaging process and is of a good practical value. At the same time, the algorithm is useful for image collecting work in other machine vision engineering.