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arXiv · 2102.05399

Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement

Prateek Garg·Murari Mandal·Pratik Narang
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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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Prateek Garg, Murari Mandal, Pratik Narang. 2021-02-10. Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement. https://arxiv.org/abs/2102.05399

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