Flying By ML -- CNN Inversion of Affine Transforms
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arXiv
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866929194914545664 |
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| author | Van Warren, L. |
| author_facet | Van Warren, L. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_17258 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Flying By ML -- CNN Inversion of Affine Transforms Van Warren, L. Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning 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. |
| title | Flying By ML -- CNN Inversion of Affine Transforms |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2312.17258 |