Damage Segmentation on High-Resolution Coating Images Using a Novel Two-Stage Network Pipeline

Abstract

The automation of inspections in aircraft engines is an ever-increasing growing field of research. In particular, the inspection and quantification of coating damages in confined spaces, usually performed manually with handheld endoscopes, comprise tasks that are challenging to automate. In this study, 2D RGB video data provided by commercial instruments are further analyzed in the form of a segmentation of damage areas. For this purpose, large overview images, which are stitched from the video frames, showing the whole coating area are analyzed with convolutional neural networks (CNNs). However, these overview images need to be divided into smaller image patches to keep the CNN architecture at a functional and fixed size, which leads to a significantly reduced field of view (FOV) and therefore a loss of information and reduced network accuracy. A possible solution is a downsampling of the overview image to decrease the number of patches and increase this FOV for each patch. However, while an increased FOV with downsampling or a small FOV without resampling both exhibit a lack of information, these approaches incorporate partly different information and abstractions to be utilized complementary. Based on this hypothesis, we propose a two-stage segmentation pipeline, which processes image patches with different FOV and downsampling factors to increase the overall segmentation accuracy for large images. This includes a novel method to optimize the position of image patches, which leads to a further improvement in accuracy. After a validation of the described hypothesis, an evaluation and comparison of the proposed pipeline and methods against the single-network application is conducted in order to demonstrate the accuracy improvements.

Keywords

• semantic segmentation
• damage inspection
• CNN
• DeeplabV3+
• endoscopic inspection
• transfer learning