视觉目标跟踪极大地推动了无人机（UAVs）的智能应用。然而，当前最先进（SOTA）的夜间无人机跟踪增强器总是忽略低光照图像中不均匀的光照分布，不可避免地导致在复杂光照场景下过度增强。为了解决这些问题，本工作提出了一种新颖的增强器，即LDEnhancer，通过光照分布抑制来增强夜间无人机跟踪。具体而言，开发了一个新颖的图像内容细化模块，在特征空间中分解光照分布信息和图像内容信息，从而实现对图像内容信息的针对性增强。接着，本工作设计了一个新的光照分布生成模块，以有效捕获光照分布。包含光照分布信息和图像内容信息的特征分别被输入到不同的参数估计模块中，用于参数图预测。最后，利用两个参数图，提出了一种创新的交织迭代调整方法，用于对低光照图像进行协作的逐像素调整。此外，构建了一个具有不均匀光照分布的具有挑战性的夜间无人机跟踪数据集，即NAT2024-2，以提供全面的评估，该数据集包含40个具有挑战性的序列，总计超过74K帧。在权威的无人机基准和提出的NAT2024-2上的实验结果表明，LDEnhancer在夜间无人机跟踪方面优于其他SOTA低光照增强器。此外，在配备NVIDIA Orin NX的典型无人机平台上的真实世界测试证实了LDEnhancer的实用性和高效性。代码可在https://github.com/vision4robotics/LDEnhancer获取。
英文摘要：
Visual object tracking has boosted extensive intelligent applications for unmanned aerial vehicles (UAVs). However, the state-of-the-art (SOTA) enhancers for nighttime UAV tracking always neglect the uneven light distribution in low-light images, inevitably leading to excessive 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.
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning
College of Computer Science and Engineering, Guilin University of Technology（桂林理工大学计算机科学与工程学院）；School of Computer Science, Fudan University（复旦大学计算机科学学院）；School of Artificial Intelligence, Sun Yat-sen University（中山大学人工智能学院）
Dual Prompt-Driven Feature Encoding for Nighttime UAV Tracking
Pratt School of Engineering, Duke University（杜克大学工程学院）；School of Mechanical Engineering, Tongji University（同济大学机械工程学院）；Shanghai Key Laboratory of Wearable Robotics and Human Machine Interaction, Tongji University（同济大学可穿戴机器人与人机交互重点实验室）；School of Computing and Data Science, The University of Hong Kong（香港大学计算与数据科学学院）
Di$^2$CycleSB: Towards High-Quality Unsupervised Nighttime Visibility Enhancement via Schrödinger Bridge Transformer
Faculty of Data Science, City University of Macau（澳门城市大学数据科学学院）；Department of Automation, Tsinghua University（清华大学自动化系）；College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）
Bridge Damage Detection from Low-Light UAV Imagery via Degradation-Aware Mixture-of-Experts Enhancement
School of Computer Science and Engineering, University of Electronic Science and Technology of China（电子科技大学计算机科学与工程学院）；Sustainability X-Lab, The University of Hong Kong（香港大学可持续发展X实验室）；School of Architecture, Building, and Civil Engineering, Loughborough University（拉夫堡大学建筑、建造与土木工程学院）；Department of Engineering Science, University of Oxford（牛津大学工程科学系）；Department of Civil and Environmental Engineering, The Hong Kong University of Science and Technology（香港科技大学土木与环境工程系）
DroneFINE: Domain-Aware Parameter-Efficient Fine-Tuning of Vision-Language Detectors for Drone Images
National College for Excellent Engineers, Beihang University, Beijing, China（北京航空航天大学优秀工程师学院）；School of Computer Science and Engineering, Beihang University, Beijing, China（北京航空航天大学计算机科学与工程学院）；School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, China（合肥工业大学计算机科学与信息工程学院）；State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China（虚拟现实技术与系统国家重点实验室）；Innovation Center for Intelligent System Cognition and Decision-Making, Beijing, China（智能系统认知与决策创新中心）；Information Science Academy of China Electronics Technology Group Corporation, Beijing, China（中国电子科技集团有限公司信息科学研究院）
