Using deep learning improve the aerial engine nondestructive radiographic tests

Zhi Hao Chen, Jyh Ching Juang

研究成果: Conference contribution

摘要

This paper aim use of deep convolutional neural networks (CNNs) with generative adversarial networks for aircraft engine X-ray cracks image classification and detection posed. On the basis of the CNNs approach requires large amounts of X-ray defect imagery data. Those data facilitate a cracks image segmentation and tracking on multiple defect of aircraft engine defection by edge detection feature extraction and classification process. The use of the deep CNNs approach deep learning model seeks to augment and improve existing automated nondestructive testing (NDT) diagnosis. Within the context of X-ray screening, limited numbers insufficient types of X-ray aircraft engine defect data samples can thus pose another problem in support vector machine (SVM) model accuracy. To overcome this issue, we employ a deep learning paradigm of generative adversarial network such that a pre-trained deep CNNs. We are primarily trained for aircraft engine defect X-ray image classification eight types where sufficient training data exists. This result are empirically show that deep learning net complex with the pre-tuned model features also more yield superior performance to human crafted features on object identification tasks. Overall the achieve result get more then 90% accuracy based on the DetectNet features model retrained with 8 types of composite material defect classifiers.

原文English
主出版物標題Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020
發行者Institute of Electrical and Electronics Engineers Inc.
頁面315-318
頁數4
ISBN(電子)9781728193625
DOIs
出版狀態Published - 2020 十一月
事件2020 International Symposium on Computer, Consumer and Control, IS3C 2020 - Taichung, Taiwan
持續時間: 2020 十一月 132020 十一月 16

出版系列

名字Proceedings - 2020 International Symposium on Computer, Consumer and Control, IS3C 2020

Conference

Conference2020 International Symposium on Computer, Consumer and Control, IS3C 2020
國家/地區Taiwan
城市Taichung
期間20-11-1320-11-16

All Science Journal Classification (ASJC) codes

  • 電氣與電子工程
  • 控制和優化
  • 儀器
  • 原子與分子物理與光學
  • 電腦科學應用
  • 能源工程與電力技術
  • 人工智慧

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