  To maximise fuel economy, bladed components in aero-engines operate close to
material limits. The severe operating environment leads to in-service damage on
compressor and turbine blades, having a profound and immediate impact on the
performance of the engine. Current methods of blade visual inspection are
mainly based on borescope imaging. During these inspections, the sentencing of
components under inspection requires significant manual effort, with a lack of
systematic approaches to avoid human biases. To perform fast and accurate
sentencing, we propose an automatic workflow based on deep learning for
detecting damage present on rotor blades using borescope videos. Building upon
state-of-the-art methods from computer vision, we show that damage statistics
can be presented for each blade in a blade row separately, and demonstrate the
workflow on two borescope videos.
