Deep Learning based Framework for Automatic Damage Detection in Aircraft Engine Borescope Inspection

Abstract

To ensure high safety in civil aviation, borescope inspection has been widely applied in early damage detection of aircraft engines. Current manual damage inspection on borescope images inevitably results in low efficiency for engine status inspection. Traditional recognition methods are inefficient for damage detection due to complicated and noisy scenarios inside them. In this paper, a deep learning based framework is proposed which utilizes the state-of-the-art algorithm called Fully Convolutional Networks (FCN) to identify and locate damages from borescope images. Our framework can successfully identify two major types of damages, namely crack and burn, from borescope images and extract their region on these images with high prediction accuracy. Moreover, by applying the fine-tuning method, the proposed framework is further optimized to significantly reduce the amount of training data. With experiments on real borescope images data from one major airline company, we validate the efficiency and accuracy of our proposed framework through comparisons with other CNN architectures for damage identification and recognition.