Research On Key Technique For Automatic Detection System

In recent years, with rapid development of infomation technique , material technique, micro-electronics technique and computer technique, inspection technique is more and more profound and abroad. The production , existance, economy communication and science research of modern human society are all related to inspection technique. Each research domain, especial biolog, ocea, communications, geology , spaceflight, weather, control, machine, traffic are all not independent to inspection technique. So the inspection technique is a very important basic technique in human advanced society.In this paper, we use the image processing, pattern recognition technology to exploit distress automatic inspection system, and carry on distress processing research in order to promote the intelligent technology development of the distress inspection.1. Firstly, the paper summarizes current situation in automatic detection technique both at home and abroad. Secondly, the paper analyzes current situation and trend of digital image capture and processing technology, adopts the digital image process system based on TI DSP.2. According to the characteristic of distress image and uses linear image enhancement, histogram equalization technology to enhance image contrast. The paper studies mean filter, SUSAN filter, median filter and gauss filter technology, and selects the fast median filter to get good smoothing result of pavement image. On the basis of pavement image preprocessing, the paper starts with the classical edge detection method, researches two kinds of improved edge detection methods: Marr-Hildreth and Canny. Then the paper studies image segmentation algorithms include: OSTU, transition region, maxim entropy and entropy correlation algorithm, and transition region, entropic correlation make the good segmentation result.3. The paper uses BP neural network to classify distress. The invariant moments are used to represent pavement distress image feature, and normalization features are inputted BP neural network classifier to train and classify. The experiment resultshows that this classifier can recognize pavement distress better when a little noise exists in pavement image. The paper also discusses the real-time ability of the algorithm, experiment shows that preprocessing algorithm fulfils real-time ability but the real-time ability of the classify method need study further.