Improving Aerial Instance Segmentation in the Dark with Self-Supervised Low Light Enhancement
This addresses the need for efficient low light enhancement in aerial vision applications, though it appears incremental as it builds on existing methods with specific optimizations.
The paper tackles the problem of low light conditions degrading aerial instance segmentation by proposing a self-supervised enhancement method that improves performance with minimal computational overhead, delivering superior results.
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.