.png){kind=icon}
.png){kind=icon}
Return
Enhanced Diffusion-Based Analysis for Fast Defect Detection in ECPT Image
.png){kind=icon}
PRE
AI
DOI：10.1109/TII.2023.3298469.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Eddy current pulsed thermography (ECPT) has attracted much attention in nondestructive testing for its noncontact and large field of view. However, the ECPT images usually suffer from the thermal diffusion blurs. The fusion of temperature spatial and temporal features is the hotspot in the new research of ECPT enhancing methods, but these two features are contradictory on the speed and the accuracy of algorithms. Specifically, spatial features process fast but perform poorly in accuracy and antinoise ability, while temporal features are usually calculated from the whole ECPT video sequence, which inevitably increases the demand for data storage and the time cost, especially in the detection of large workpieces, such as engine blades or pressure pipelines. In this article, an enhanced diffusion-based method (EDBM) is proposed to solve this issue, which maps the temporal features through the spatial features of a single ECPT image, significantly reduces the input data volume and shows great potential in ECPT online detection. Experiments on multiple artificial and natural samples verify that, compared with raw ECPT image, the proposed EDBM can reduce root-mean-square error by 51.7%-86.5% (73.2% on average) and improve signal-to-noise ratio by 3.70-16.8 times (6.82 times on average), which performs better than the commonly spatial-based and temporal-based ECPT enhancement algorithms, such as enhanced Canny and independent component analysis, close to the latest sparse-model decomposition methods, but with two orders of magnitude less time cost.
Keywords:
Heating systems
Coils
Temperature distribution
Feature extraction
Eddy currents
Cooling
Thermal analysis
Defect detection
eddy current pulsed thermography (ECPT)
image enhancement
nondestructive testing (NDT)
Journal
IF:
9.9
Papers:
8.3K
Citations:
6.0W