Research On Defect Classification And Image Recognition Methods Of The Welding Film Using X-ray Detecting

With the development of X-ray detection and image processing technology, welding defects detection gradually transits from artificial detection to computer intelligence recognition. Analysis and recognition digitized welding image by computer is approved by the people in detection efficiency,economic profits,convenient and pragmatic, etc.The paper is based on the actual project demand and takes the X-ray detection film as the research object, useing the method of image processing give the quantitative and qualitative description of welding defects, applied Visual C++ build up welding image recognition system based on the statistics decision tree, carry out the assessment automatically.According to chatacteristic of the weld image, divided the image processing system into noisy reducing, image enhancement, edge detection and image segmentation. After various methods of image processing are compared, the method of the adaptive median filtering is used for decreasing the noise and filtering; the methods of the histogram equalization and the blur enhancement are introduced to make the image enhancement; in the part of the edge detection, the genetic algorithm is introduced to the gradient operators to extract the edge of the welding; at last, the threshold segmentation, edge detection and mathematical morphology of image segmentations are compared, the variance ratio of interclass and intraclass segmentation, and mathematical morphology are used for the image segmentation of the welding to extract the effective area of the welding.After the image segmentation, multi-defect tracking and filling are designed for carrying out image recognition; Choosing feature parameter is a premise of the defects recognition. The paper determines the feature parameter which can reflect the nature of the defects by analyzing the image characteristic of the defects.at last, the defects recognition and classification are realized by the tree classifier.