To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an au...To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.展开更多
文摘To solve the problems of low precision of weak feature extraction,heavy reliance on labor and low efficiency of weak feature extraction in X-ray weld detection image of ultra-high voltage(UHV)equipment key parts,an automatic feature extraction algorithm is proposed.Firstly,the original weld image is denoised while retaining the characteristic information of weak defects by the proposed monostable stochastic resonance method.Then,binarization is achieved by combining Laplacian edge detection and Otsu threshold segmentation.Finally,the automatic identification of weld defect area is realized based on the sequential traversal of binary tree.Several characteristic analysis dimensions are established for weld defects of UHV key parts,including defect area,perimeter,slenderness ratio,duty cycle,etc.The experiment using theweld detection image of the actual production site shows that the proposedmethod can effectively extract theweak feature information ofweld defects and further provide reference for decision-making.
