| From the view of economy and utility, In this paper, a suit of examination system of industrial vision is designed. Industrial vision can be divided into three classes: inspecting, orientation and setting-up. This system belongs to the class of inspecting, this system is a set of automatic inspecting devices, which is used to classify and recognize industrial parts. During the course of industrial manufacture, the determinacy of the industrial environment and illuminating conditions makes the ideas possible that industrial parts can be distinguished and classified rapidly and accurately. The system introduced below is the result of this idea. In automobile parts product line, if we want to classify products and check them whether they are up to grade or not, all process is required to be finished automatically in the process line and is not permitted to be checked manually on the off-line. The examination system of industrial vision can finished this work automatically and make production faster and more efficient. This system gets original information by image sensor, which avoids direct contact with objects being processed and manual inspection. So this system can meet the requirement that industrial parts should be inspected on-line. At the same time, when software is programmed, simple steps can be designed according to the shape of industrial parts to attain the high-speed and high-efficient purpose. In this task, we presented and designed a system that can classify and recognize circular and quadrate parts and the intension image of industrial parts is the object of the system, human-computer communication mode is adopted to identify the parts. In the part of software design, pattern recognition of selected parts is firstly carried out, it includes three phases: image pretreatment, feature extraction, systematizer design. In the phase of image pretreatment, the main jobs of this system includes dot operation, image swell, positive chiasma transform, edge extraction and edge swell, outline track, etc. Because the visual system itself is a neural system, systematizer designed in the paper adopts BP neural network to accomplish computer image identification, the system has some advantages over the traditional one, but with the extensive application of BP neural network, the problems existing in BPneural network come forth increasingly. There are some main problems, such as easily forming local extraordinary smallness and not having integral extraordinary excellence, training easily into paralysis, quite slow convergence speed, etc. Aiming at overcoming the limitation of the BP algorithm, this paper brings forward a sort of improved BP algorithm. A experienced equation which is summarized by many experiments is used to determine the number of mesosphere nerve cell and a sort of new square-sum function of errors is adopted. Its characteristic is that weight errors of possible exceptional point is less. Accordingly, the effect of errors of possible exceptional point is reduced, which make actual function relation simulation easier. With having tutor study style, this system adopts a sort of new error function to adjust its parameter systemically according to the status of sample in the course of training. When the algorithm is applied in the identification of industrial parts, comparison with the traditional BP neural network the recognition time will be shortened 2.8 second, and the recognition accuracy can reach more than 81%. After the classification and recognition of selected parts, a disfigurement inspection subsystem is also designed by this task. The accuracy of disfigurement inspection can reach more than 91%. The experiment result indicates, if applying this system to industrial production line can improve the productivity and guarantee the quality of products.
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