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Enhancing Nighttime UAV Tracking with Light Distribution Suppression

Code and demo videos of LDEnhancer---a low-light enhancer towards facilitating UAV tracking at night with uneven light distribution.

Abstract

Visual object tracking has boosted extensive intelligent applications for unmanned aerial vehicles (UAVs). However, the state-of-the-art (SOTA) enhancer for nighttime UAV tracking always neglects the uneven light distribution in low-light images, inevitably leading to over-enhancement in scenarios with complex illumination. To address these issues, this work proposes a novel enhancer, i.e., LDEnhancer, enhancing nighttime UAV tracking with light distribution suppression. Specifically, a novel image content refinement module is developed to decompose the light distribution information and image content information in the feature space, allowing for the targeted enhancement of the image content information. Then this work designs a new light distribution generation module to capture light distribution effectively. The features with light distribution information and image content information are fed into the different parameter estimation modules, respectively, for the parameter map prediction. Finally, leveraging two parameter maps, an innovative interweave iteration adjustment is proposed for the collaborative pixel-wise adjustment of low-light images. Additionally, a challenging nighttime UAV tracking dataset with uneven light distribution, namely NAT2024-2, is constructed to provide a comprehensive evaluation, which contains 40 challenging sequences with over 74K frames in total. Experimental results on the authoritative UAV benchmarks and the proposed NAT2024-2 demonstrate that LDEnhancer outperforms other SOTA low-light enhancers for nighttime UAV tracking. Furthermore, real-world tests on a typical UAV platform with an NVIDIA Orin NX confirm the practicality and efficiency of LDEnhancer. The code is available at https://github.com/vision4robotics/LDEnhancer.

This figure shows the workflow of our tracker.

Demo

• 📹 Demo of real-world tests.
• The demo will be released in the coming period.

Download the datasets：

train_dataset

• NAT2021_light: Dropbox,Baidu (code: a2b4)

test_dataset

• NAT2024-2: Dropbox, Baidu.

Contact

Liangliang Yao

Email: 1951018@tongji.edu.cn

Changhong Fu

Email: changhongfu@tongji.edu.cn

Demonstration running instructions

Requirements

1.Python 3.7

2.Pytorch 1.0.0

3.opencv-python

4.torchvision

5.cuda 10.2

Download the package, extract it and follow two steps:

1. Put test images in data/test/, put training data in data/train/.

2. For testing, the tracking performance is evaluated, run:

   cd SOT-test
   python test.py
   

3. For training, run:

   cd LDEnhancer
   python lowlight_train.py
   

• In our experiment, the best weight is the 66th epoch.

Acknowledgements

• The code for training is constructed on Darklighter. We sincerely thank the contribution of Junjie Ye for their previous work.

• The code for evaluation is implemented based on SNOT and CDT. We would like to express our sincere thanks to the contributors.

• We sincerely thank SiamAPN, SiamAPN++, SiamRPN++, and LPAT trackers for their efforts.

• We sincerely thank DarkLighter, HighlightNet, SCT and DCE++ enhancers for their efforts.