Flying By ML -- CNN Inversion of Affine Transforms
This addresses the problem of manual gauge reading in aviation, but it is incremental as it builds on existing CNN methods for image analysis.
The paper tackles automating cockpit gauge reading by using a CNN to invert affine transformations and deduce aircraft states from instrument images, achieving validation with synthetic images of a turn-and-bank indicator.
This paper describes a machine learning method to automate reading of cockpit gauges, using a CNN to invert affine transformations and deduce aircraft states from instrument images. Validated with synthetic images of a turn-and-bank indicator, this research introduces methods such as generating datasets from a single image, the 'Clean Training Principle' for optimal noise-free training, and CNN interpolation for continuous value predictions from categorical data. It also offers insights into hyperparameter optimization and ML system software engineering.