AE-RTISNet: Aeronautics engine radiographic testing inspection system net with an improved fast region-based convolutional neural network framework

Zhi Hao Chen, Jyh Ching Juang

Research output: Contribution to journalArticlepeer-review

Abstract

To ensure safety in aircraft flying, we aimed to use deep learning methods of nondestructive examination with multiple defect detection paradigms for X-ray image detection. The use of the fast region-based convolutional neural network (Fast R-CNN)-driven model was to augment and improve the existing automated non-destructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers and insufficient types of X-ray aeronautics engine defect data samples can, thus, pose another problem in the performance accuracy of training models tackling multiple detections. To overcome this issue, we employed a deep learning paradigm of transfer learning tackling both single and multiple detection. Overall, the achieved results obtained more than 90% accuracy based on the aeronautics engine radiographic testing inspection system net (AE-RTISNet) retrained with eight types of defect detection. Caffe structure software was used to perform network tracking detection over multiple Fast R-CNNs. We determined that the AE-RTISNet provided the best results compared with the more traditional multiple Fast R-CNN approaches, which were simple to translate to C++ code and installed in the Jetson TX2 embedded computer. With the use of the lightning memory-mapped database (LMDB) format, all input images were 640 × 480 pixels. The results achieved a 0.9 mean average precision (mAP) on eight types of material defect classifier problems and required approximately 100 microseconds.

Original languageEnglish
Article number8718
Pages (from-to)1-14
Number of pages14
JournalApplied Sciences (Switzerland)
Volume10
Issue number23
DOIs
Publication statusPublished - 2020 Dec 1

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint

Dive into the research topics of 'AE-RTISNet: Aeronautics engine radiographic testing inspection system net with an improved fast region-based convolutional neural network framework'. Together they form a unique fingerprint.

Cite this