Research on UAV Point Landing Based on Visual Navigation

Meng, Chao; He, Boyong; Lin, Hui; Wu, Liaoni

doi:10.1109/gncc42960.2018.9018958

Cited by 2 publications

(1 citation statement)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Object tracking has been widely used in a variety of sectors, most representative in UAV applications for target following [1], autonomous landing [2], and self-localization [3]. SOTA tracking approaches [4], [5] have achieved outstanding performance under favorable illumination.…”

Section: Introductionmentioning

confidence: 99%

HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking

Fu¹,

Dong²,

Ye³

et al. 2022

Preprint

View full text Add to dashboard Cite

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 state-of-theart (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.

show abstract