Guanghua Zheng1,2, Chaolin Luo1,3, Mengen Shen1,*, Wanzhong Lv4, Wenbo Jiang4, Weibo Yang2
Energy Engineering, Vol.120, No.4, pp. 985-1000, 2023, DOI:10.32604/ee.2023.024372
- 13 February 2023
Abstract 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 More >
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