Name
Title | ||
|---|---|---|
Spatio-Temporal 3-D Residual Networks for Simultaneous Detection and Depth Estimation of CFRP Subsurface Defects in Lock-In Thermography |
Abstract | ||
|---|---|---|
Nondestructive thermography is a high-speed, low-cost, and safe solution for subsurface defects detection of carbon fiber reinforced polymer (CFRP) materials, providing essential quality control in aerospace, automobile, and sports industries. In this article, we build a reflective lock-in thermography system and construct a dataset that contains real-captured thermal image sequences of CFRP samples with various simulated internal defects under different excitation frequencies. Then, we present a novel 3-D convolutional neural network (CNN) model incorporating a combination of spatial and temporal convolutional filters and batch-size independent group normalization (GN) as a unified framework to process thermal image sequences captured by lock-in thermography for simultaneous subsurface defect detection and depth estimation. Finally, we define a multitask loss function to perform end-to-end training of both defect detection and depth estimation tasks based on the real-captured infrared sequences. Comparative experiments are carried out on CFRP specimens with artificial defects of various sizes/shapes and at different depths. Qualitative and quantitative results illustrate that our 3-D CNN model is capable of predicting accurate locations and depths of subsurface defects and performs favorably against the hand-crafted and CNN-based methods in lock-in thermography for individual defect detection and depth estimation tasks. The captured dataset and the source codes will be made publicly available. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/TII.2021.3103019 | IEEE Transactions on Industrial Informatics |
Keywords | DocType | Volume |
Carbon fiber reinforced polymer (CFRP),defect detection,depth estimation,lock-in thermography,nondestructive testing,spatio-temporal 3-D convolution | Journal | 18 |
Issue | ISSN | Citations |
4 | 1551-3203 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yafei Dong | 1 | 0 | 0.34 |
| Chenjie Xia | 2 | 0 | 0.34 |
| Jiangxin Yang | 3 | 40 | 7.68 |
| Yaolong Cao | 4 | 16 | 7.86 |
| Yanpeng Cao | 5 | 30 | 6.32 |
| Xin Li | 6 | 3 | 2.48 |