Recent years have witnessed the fast evolution and promising performance of
the convolutional neural network (CNN)-based trackers, which aim at imitating
biological visual systems. However, current CNN-based trackers can hardly
generalize well to low-light scenes that are commonly lacked in the existing
training set. In indistinguishable night scenarios frequently encountered in
unmanned aerial vehicle (UAV) tracking-based applications, the robustness of
the state-of-the-art (SOTA) trackers drops significantly. To facilitate aerial
tracking in the dark through a general fashion, this work proposes a low-light
image enhancer namely DarkLighter, which dedicates to alleviate the impact of
poor illumination and noise iteratively. A lightweight map estimation network,
i.e., ME-Net, is trained to efficiently estimate illumination maps and noise
maps jointly. Experiments are conducted with several SOTA trackers on numerous
UAV dark tracking scenes. Exhaustive evaluations demonstrate the reliability
and universality of DarkLighter, with high efficiency. Moreover, DarkLighter
has further been implemented on a typical UAV system. Real-world tests at night
scenes have verified its practicability and dependability.

本文提出了一种名为 DarkLighter 的低光图像增强器，采用轻量级的 ME-Net 网络，用于提高无人机跟踪器在夜间、低光环境下的鲁棒性。实验证明，DarkLighter 的普适性和可靠性很高，并已在典型的 UAV 系统上实现，夜间场景的实际测试表明其实用性和可靠性。