Abnormity Detection System Based On Template And Study Of Its Key Technologies

With the demand for abnormity detection increased day by day in industrial and military fields,high-performance and high reliability image processing algorithms become more mature, which promotes machine vision technology widely used in abnormity detection.Aimed at the actual demand and a number of issues needed to be addressed, this paper carried out the research, designed and implemented a template-based abnormity detection system.Aimed at the problem that a single abnormity detection algorithm can't be universally suitable for various application environments and detection objects of different characteristics, we proposed the plant strategy model to design the abnormity detection module of the system and realized two kinds of abnormity detection methods:registration matching using gray information and alignment matching using contour information, which are suitable for different application situation.Through the plant strategy model and some corresponding configuration files, whether application environment or the characteristic of the detection object changes,the client can dynamically create,configure,and load the algorithm meeting the current demand of abnormity detection to ensure that the abnormity detection module can get a good balance among rapidity, stability and accuracy in the current application.Aimed at the problem that the abnormity detection method of template matching using gray information is easy to be influenced by camera vibration and illumination change, this paper firstly designed a fault-tolerant block projection matching method to realize the image correction of camera vibration, and then proposed the Wallis transformation method to eliminate illumination change, which is more real-time than the mothed using Gaussian filter and polynomial fitting introduced before. Finally, carrying out abnormity decision based on weighted multi-areas difference matrix.The practice shows that in the situation of small camera vibration and successive illumination change, this method can quickly and accurately detect abnormity.Aimed at the characteristic that high accuracy contour point segmentation is necessary for the abnormity detection method of alignment matching using contour information, this paper firstly compared common edge detection algorithm, and proposed the edge extraction method using canny operator to overcome the problem that traditional edge detection operator is easy to be influenced by noise and the problem that the contour is too thick. And then boundary tracing is used for constructing target contour and calculating chain code entropy, chain code spatial distribution entropy and contour moment. Finally, coarse matching based on global feature similarity is used for finding all areas in the image that contain the detection object, and then fine matching based on contour alignment is used for carrying out abnormity decision to precisely locate abnormity in the contour.Aimed at the protection demand of injection mold, this paper realized the template-based abnormity detection system. The practice shows that abnormity detection algorithms proposed by this paper can guarantee real-time and accuracy abnormity detection and achieve the purpose of the study by phases.