Spatio-Temporal 3-D Residual Networks for Simultaneous Detection and Depth Estimation of CFRP Subsurface Defects in Lock-In Thermography

作者：Yafei Dong, Chenjie Xia, Jiangxin Yang, Yanlong Cao, Yanpeng Cao, Xin Li · 发表于：IEEE Transactions on Industrial Informatics · 年份：2021 · DOI：10.1109/tii.2021.3103019 · 被引用次数：38 · 研究领域：Thermography and Photoacoustic Techniques、Industrial Vision Systems and Defect Detection、Structural Health Monitoring Techniques

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 ...