23 May 2013 Self-adaptive characteristics segmentation optimized algorithm of weld defects based on flooding
Author Affiliations +
In order to improve the accuracy and efficiency of weld defect segmentation in automatic radiographic nondestructive testing and evaluation(NDT&E), an effective weld defect segmentation algorithm based on flooding has been developed, which has the self-adaptive characteristics. Firstly, the defect’s feature points are extracted from the scale space of radiographic films. Based on the information of defect points, the seed points and seed domains of defect discrimination are adaptively determined, in which the defect segmentation seed will be searched. Then, aiming at the sparsity of weld defects and canyon characteristics of 3D topographic map of defect regions, the drip-watering and water flooding have been used for reference. The flooding is carried out by using line-flooding algorithm, in which water starts from defect seed points and flows to the neighbor regions in order. On the basis of the flooding-area change and flooding-level ascending velocity, the defect segmentation threshold values are determined and the weld defects also are segmented from the radiographic films. At last, the comparative experiments have been carried out to compare the proposed algorithm with the watershed segmentation algorithm and background subtraction segmentation algorithm. And the experiment results confirm that the proposed algorithm obviously improves the accuracy and efficiency of weld defect’s segmentation.
© (2013) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Changying Dang, Changying Dang, Jianmin Gao, Jianmin Gao, Zhao Wang, Zhao Wang, Fumin Chen, Fumin Chen, "Self-adaptive characteristics segmentation optimized algorithm of weld defects based on flooding", Proc. SPIE 8745, Signal Processing, Sensor Fusion, and Target Recognition XXII, 87451T (23 May 2013); doi: 10.1117/12.2015368; https://doi.org/10.1117/12.2015368

Back to Top