  Low-light environments have posed a formidable challenge for robust unmanned
aerial vehicle (UAV) tracking even with state-of-the-art (SOTA) trackers since
the potential image features are hard to extract under adverse light
conditions. Besides, due to the low visibility, accurate online selection of
the object also becomes extremely difficult for human monitors to initialize
UAV tracking in ground control stations. To solve these problems, this work
proposes a novel enhancer, i.e., HighlightNet, to light up potential objects
for both human operators and UAV trackers. By employing Transformer,
HighlightNet can adjust enhancement parameters according to global features and
is thus adaptive for the illumination variation. Pixel-level range mask is
introduced to make HighlightNet more focused on the enhancement of the tracking
object and regions without light sources. Furthermore, a soft truncation
mechanism is built to prevent background noise from being mistaken for crucial
features. Evaluations on image enhancement benchmarks demonstrate HighlightNet
has advantages in facilitating human perception. Experiments on the public
UAVDark135 benchmark show that HightlightNet is more suitable for UAV tracking
tasks than other SOTA low-light enhancers. In addition, real-world tests on a
typical UAV platform verify HightlightNet's practicability and efficiency in
nighttime aerial tracking-related applications. The code and demo videos are
available at https://github.com/vision4robotics/HighlightNet.
