Research On CV-based Bridge Surface Block Detection And Measurement Technology

The transportation is a national economic lifeline, and paths and bridges enable the transportation to be the unimpeded carrier. Especially, bridges are playing a very important role. Along with their service time increase, the bridge structure can appear unavoidably various weary and damage, which lead to malignant accident, huge economic loss and person injures. But the majority bridge fault of construction mainly produces in the bridge base, where both manual examination and automatic detection are especially difficult. Therefore, how to carry the regular inspection and the health check-up on the bridge structure has the important theory significance and the practical value.Regarding the large-scale concrete bridge, the bridge crack is one of the main contents in bridge detection and measurement. But the simplex manual range estimate examination method is low efficiency. Therefore applying the digital image technology and the pattern recognition methods in the bridge surface defect examination, in order to enhance the working efficiency, and it is extremely important for the external evaluation of bridge surface flaw.In the thesis, according to the health evaluation and inspecting method of modern bridges, the digital image technology and the pattern recognition methods combined with existing criterions are provided to detect the surface-flaw in bridges. And discuss some keys thereinafter: (1) High speed CCD and the image-capturing card are utilized to gather the flaw images: By analyzing the uneven illuminations in image capturing, suitable lighting mode is chosen. (2) The median filter and the weighted average algorithm are used to getting rid of noises. And the iteration clipper algorithm and morphology are carried to preserve more available characters of image. (3) After the "block" processing carried on the image, the thesis proposes two block-based BP neural network methods: image-based nerve network method (INN) and histogram-based neural network method (HNN). These two methods are all developed based on the...