TL;DR
DarkLighter is a low-light image enhancer designed to improve UAV tracking performance in dark scenes by estimating illumination and noise maps, enhancing visibility and robustness of CNN-based trackers in night conditions.
Contribution
This work introduces DarkLighter, a novel low-light enhancer with a lightweight estimation network, improving UAV tracking in dark environments and demonstrating practical effectiveness in real-world tests.
Findings
DarkLighter significantly improves tracking accuracy in low-light UAV scenes.
The method is efficient and suitable for real-time UAV applications.
Real-world night tests confirm its practicality and robustness.
Abstract
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…
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