| Computer vision is a modern detecting technology, the actual engineering project as research its object, computer vision method as its base, and adapts image processing, precise measurement, intelligent control and pattern recognition. It has the characters of gaining a great deal of information quickly, and being apt to integrate with design and manufacture control information. Intelligence, digitization, miniaturization, networking and multi-function can be realized. Hold the ability of online detecting, real-time analysis and control. At present, computer vision detecting technology is used as robot, industrial detecting, object recognition, medical image analysis, military navigation and traffic control etc.Automotive industry is used as the mainstay industry of national economy. Its manufacturing engineering reflects a level of a national basic industry. Whether detecting method and technology of automotive productive enterprise are advanced or not, they have become a very important factor of regulating realization of automobile manufacture and improvement of production quality.The automobile frame is a very important part of automobile steering system. It's the major bearing structure; at the same time, motor, turn system, transmission system and brake system connect with the auto rack girders through the assembly holes in it. Before the total assembly of automobile, dimension, position and numbers of assembly holes are required to be detected. If auto rack girder with disqualified holes is put on assembly lines, this will lead to some parts or the whole automobile can't be assembled and so on serious problems. So the detecting results of dimension, position and numbers of assembly holes affect the execution of automobile assembly technology. At present, there isn't online detecting system of assembly holes quality for auto rack girders in my country. Due to missing manufacture of holes or disqualified dimension and etc, corporations break off production usually. This led to the huge economic loss.The goal of this item is to change the out-dated status of online detecting on domestic auto rack girders quality, and hopes to develop domestic technology and shorten overseas and domestic gap.This paper associated the technology innovative item of corporation with Jilin province technology application basic research funding item"Research of The Auto Rack Girders Detecting System"(item No.20060534), the auto rack girders being used as the applied objects, and researched online detecting technology of dimension, position and kinds recognition of the assembly holes.This paper analyzed the image mosaic method; for phase related match algorithm in frequency domain, introduced an improved image matching algorithm based on overlapped region. Counting quantity is related to dimension of image, and unrelated to translation quantity among images. When carrying on image mosaic, this algorithm solved mismatching because of existence of background noises, conquered the effect of light transformation and lens geometric distortion, and had the stronger anti-noise ability.For having been matching auto rack girder panorama images, this paper introduced a weight smooth fusion algorithm based on boundary maintenance. This algorithm not only can keep boundary character, but also make region boundary clear. To improve the processing velocity, introduced an extraction algorithm of assembly holes based on local region, namely, carry on local region marking for the region of meeting holes condition; for character extraction of circles, only search in the region with assembly holes. By way of this method, decrease the searched regions, save the time of edge detection and improve the detecting velocity of auto rack girders.This paper proposed a method of combination non-linear distortion correction and double-linear transformation, to calibrate camera internal parameters and correct geometric distortion. The method solved the problem of circular holes becoming oval in the auto rack girders, because imaging plane of camera isn't parallel to the under-detecting auto rack girder plane. Using standard template as reference, through the relative calibration, accuracy and reliability of calibration meet the design demands.This paper proposed a method of ART neural network to recognize the kinds of multi-type auto rack girders, and improved the network structure and joint weight of ART2 neural network. By this way, neural network can recognize the input vectors in proportional relation, recognition of network becomes stronger. Through the combination ART2 and D-S evidence theory, complete the evidence accumulation of multi-character data, to decrease the uncertainty of auto rack girders recognition, make up for the shortage of single-character template information and improve the recognition rate.According to the technology demands of the auto rack girders online detecting, this paper researched and developed the computer vision detecting system of auto rack girders, and found the standard database of auto rack girders images. Utilizing the research results of this paper, on-line detected the dimension and position of assembly holes and kinds of auto rack girders for 5 types auto rack girders test pieces. Detecting results are actual.Experimental results indicate, online detecting system of auto rack girders which was researched and developed by this paper has higher detecting accuracy, at the same time may meet the demands of online detecting velocity. The research outcome of this paper, for online detecting of the auto rack girder with computer vision, has the theoretical and practical value.
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