Abstract
It is vital and essential to accurately and timely detect various damages of aircraft engines in civil aviation. Currently, aircraft engines are manually inspected via borescope images by aircraft maintenance technicians. This process is time-consuming and prone to error due to human factors. The aim of this paper is to automate the aircraft engine inspection, and this work presents a deep learning framework with a context encoder neural network structure such that the damaged structures can be accurately segmented from borescope images. Moreover, the proposed network structure is further optimized through an orthogonal-array-based method. With the real borescope images collected from a commercial airline company, the proposed framework is compared with existing deep-learning-based methods from various aspects. The experimental results validate that various damages can be automatically detected and recognized with high accuracy and efficiency by the proposed solution.
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