Chen, Z.-H.; Juang, J.-C. Attention-based YOLOv4 Algorithm in Non-destructive Radiographic Testing for Civic Aviation Maintenance. Preprints2021, 2021040653. https://doi.org/10.20944/preprints202104.0653.v1
APA Style
Chen, Z. H., & Juang, J. C. (2021). Attention-based YOLOv4 Algorithm in Non-destructive Radiographic Testing for Civic Aviation Maintenance. Preprints. https://doi.org/10.20944/preprints202104.0653.v1
Chicago/Turabian Style
Chen, Z. and Jyh-Ching Juang. 2021 "Attention-based YOLOv4 Algorithm in Non-destructive Radiographic Testing for Civic Aviation Maintenance" Preprints. https://doi.org/10.20944/preprints202104.0653.v1
Abstract
The aim of this paper is to use deep learning tools to innovate pre-trained object detection models to improve the accuracy of non-destructive testing (NDT) of civil aviation maintenance. First, this thesis classifies object defects for NDT, such as cracks, undercut, etc. Nowadays, thesis surveys innovation deep-learning methods technology is used to improve the defect detection performance inferencing capability, increase the accuracy and efficiency of automatic identification which in enhanced the safety and reliability of aircraft fuselage in future, mark hidden cracks and solve the challenges that cannot be identified by manual inspection. Second, recent mainstream techniques the YOLOv4 neural network to the graphics card GPU core operator to speed up the recognition of defect images is being applied to the non-destructive inspection process of aircraft maintenance on A, C and D-Level, fully validating the deep learning model's powerful defect detection target capability. The attention-based YOLOv4 algorithm is improved by applying a one-stage attention mechanism to the YOLOv4, thereby improving the accuracy of the innovation model. Finally, thesis improved YOLOv4 based on an attention mechanism is proposed for object detection NDT via the deep learning method to effectively improve and shorten the inspection anomaly detection method for automatic detection sensor systems.
Keywords
CNN, YOLOv4, NDT, Object Detection
Subject
Engineering, Automotive Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.