The Uneven Illumination Of Steel Pipe Image Algorithm Research Based On Machine Vision

Circular and classes of circular pipe is widely used in geological oil exploration, construction and other industries, due to a large amount of disordered pipe by the light, placement, angle, diameter size and the influence of complex factors such as the working environment, identify and count it has been a complex task. The traditional manual counting method exists obvious shortcomings, the intensity of labor is big, and inefficient. In order to solve this problem, an urgent need to work out the statistical precision automatic equipment. This paper, by using digital image processing and computer vision algorithms for steel pipes to count.For a large number of steel placed disorderly, uneven illumination and multi-shadow phenomenon, an improved local dynamic threshold algorithm is put forward,using the algorithm for image segmentation to obtain a more exact binary image; then the binary image with Gauss-Laplace edge detection operator, the algorithm not only to detect the edge, and eliminated noise to a certain extent; due to the radius of each steel pipe in steel pipe image were similar, for counting precision of steel tube, this paper, by using distance transformation to get approximate scope radius, and then using the hough transform to identify and count steel pipe, so not only can reduce the dimension of hough transform parameter space and running time, also can improve the recognition rate of steel pipe.Firstly, on MATLAB R2010 b platform for testing the performance of each algorithm in this paper, experimental results show that the algorithm can identify a large number of steel disorderly counting, the counting system is implemented in a complete basis vs2013 platform and Open CV computer vision library, the use of the system for detecting steel pipe image, accurate rate of 97%.