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In high-speed rail vehicles, there are many kinds of parts, each part has an important impact on the normal and stable operation of vehicles. The traditional inspection takes a long time and is prone to errors, and the inspection efficiency is low. The wrong interior decoration parts will cause different degree of rework, which greatly reduces the production efficiency. At present, there are few researches related to visual inspection in the field of shape and location dimension detection of high-speed railway parts. In view of the large-scale and unfixed characteristics of high-speed railway parts, a complete visual inspection method for shape and position dimensions of high-speed railway parts is proposed. Firstly, the interested area of parts is segmented from snake dynamic contour model, then perspective projection transformation is used to solve the problem that parts cannot be fixed, then use Sauvola local binary method to segment features, at last, extract contours and measure the shape and size of parts .Three kinds of parts on high-speed rail are tested and verified. The experimental results show that the visual based shape and position dimension detection of high-speed rail parts has better detection efficiency and scalability, which can be used in the shape and position dimension warehousing inspection of various parts of high-speed rail.