Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement
Year: 2021
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Garg, Prateek, Mandal, Murari, Narang, Pratik
Institution: Birla Institute of Technology and Science, Pilani
Language: English
Topic: Image Enhancement Techniques
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3127313735