BladeSynth

Damage Detection and Assessment in Aircraft Engines with Synthetic Data

Master thesis (2022)

Authors

C. Feng Electrical Engineering, Mathematics and Computer Science

Contributors

J.C. van Gemert Pattern Recognition and Bioinformatics - EEMCS (mentor)
Justin Dauwels Signal Processing Systems - EEMCS (graduation committee member)
J.H.G. Dauwels Signal Processing Systems - EEMCS (graduation committee member)
N. Tömen Pattern Recognition and Bioinformatics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Chengming Feng
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Published Date

29-08-2022

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Deep learning has been widely implemented in industrial inspection, such as damage detection from images. However, training deep networks requires massive data, which is hard to collect and laborious to annotate, especially in the aviation scenario of aircraft engines. To alleviate the demand for annotated data, we create BladeSynth - a large synthetic image dataset for detecting damage from aircraft engines, and empirically evaluate the transferability of state-of-the-art Scaled-YOLOV4 from synthetic to real world by pre-training on synthetic data and fine-tuning on real data. Our experiments show that pre-training on synthetic data improves the performance in damage detection in aircraft engine images.

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