Improving Aerial Instance Segmentation in the Dark with Self-Supervised
Low Light Enhancement
- URL: http://arxiv.org/abs/2102.05399v1
- Date: Wed, 10 Feb 2021 12:24:40 GMT
- Title: Improving Aerial Instance Segmentation in the Dark with Self-Supervised
Low Light Enhancement
- Authors: Prateek Garg, Murari Mandal, Pratik Narang
- Abstract summary: Low light conditions in aerial images adversely affect the performance of vision based applications.
We propose a new method that is capable of enhancing the low light image in a self-supervised fashion.
We also propose the generation of a new low light aerial dataset using GANs.
- Score: 6.500738558466833
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Low light conditions in aerial images adversely affect the performance of
several vision based applications. There is a need for methods that can
efficiently remove the low light attributes and assist in the performance of
key vision tasks. In this work, we propose a new method that is capable of
enhancing the low light image in a self-supervised fashion, and sequentially
apply detection and segmentation tasks in an end-to-end manner. The proposed
method occupies a very small overhead in terms of memory and computational
power over the original algorithm and delivers superior results. Additionally,
we propose the generation of a new low light aerial dataset using GANs, which
can be used to evaluate vision based networks for similar adverse conditions.
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