An Algorithm Of Auto-hub Shape Recognition Based On Machine Vision

Auto-hub automatic recognition system is mainly a kind of machine vision system, which can recognize different types of various auto-hubs on the assembly lines. It has many advantages, such as non-contact, online real-time, high precision, fast speed and higher anti-interference. It can classify the casting products accurately in terms of actual demands. The system fits the demand of rapid development of economic growing and industrial development. It has the prosperous application future.In the research, we use machine vision technology and image processing technology to classify different types and different shapes auto-hubs. Basing on the capture of auto-hubs images using CCD sensors and image processing cards, we preprocess auto-hub images firstly, then segment images and extract features, and finally match the classification to realize the recognition process. The main research contents consist of a few parts:converting true-color images into eight gray images based on matlab in the preprocessing stage, dealing with the noises which mainly exist in the burr of auto-hub inside using median filter. Besides these, we use the method of global threshold to segment the denoised images, combined with the characteristics of noise reduction and segmentation image, use the characteristics of the binary morphology to eject the noise of the burr, smooth the image further and then extract the edge of image. Comprehensive using fitting method,binary morphology, nine features are extracted:the circle radius, the center of the image of the holes, central hole area, wheel image aperture count, wheel hub hole inertia area and four characteristics of ideal images. In the research, we also verify the algorithm with the more relevant steps involved.Auto-hub automatic recognition system can well meet the actual needs of the current rapid economic development period of mass production and fast speed for on-line recognition and classification. It really overcomes the disadvantages of the traditional manual classification, effectively reduces misjudgment phenomenon. The research work for shape recognition of machine vision systems can realize the automation of some industrial production manufactured. The recognition method can achieve a variety of hub in the conveyor belt random mixed flow under the condition of the automatic recognition and classification, effective recognition accuracy in 90%.