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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