In low-light conditions, UAV localization faces substantial challenges due to reduced visibility, elevated noise levels, and diminished contrast. To address these issues, we propose a low-light-optimized visual localization framework that integrates an attention-based image enhancement module, a robust feature extraction network tailored for degraded environments, and a lightweight pose estimation algorithm that fuses geometric and convolutional features. Extensive evaluations on both real-world and synthetic low-light datasets reveal significant improvements in accuracy, noise resilience, and adaptability to dynamic lighting. Moreover, experimental results validate the framework’s feasibility for applications in night operations, urban air traffic management, and disaster response, thereby effectively overcoming the critical limitations of UAV positioning under low-light conditions.

Nighttime semantic segmentation represents a challenging frontier in computer vision, made particularly difficult by severe low-light conditions, pronounced noise, and complex illumination patterns. These challenges intensify when dealing with Unmanned Aerial Vehicle (UAV) imagery, where varying camera angles and altitudes compound the difficulty. In this paper, we introduce NoctuDroneNet (Nocturnal UAV Drone Network, hereinafter referred to as NoctuDroneNet), a real-time segmentation model tailored specifically for nighttime UAV scenarios. Our approach integrates convolution-based global reasoning with training-only semantic alignment modules to effectively handle diverse and extreme nighttime conditions. We construct a new dataset, NUI-Night, focusing on low-illumination UAV scenes to rigorously evaluate performance under conditions rarely represented in standard benchmarks. Beyond NUI-Night, we assess NoctuDroneNet on the Varied Drone Dataset (VDD), a normal-illumination UAV dataset, demonstrating the model’s robustness and adaptability to varying flight domains despite the lack of large-scale low-light UAV benchmarks. Furthermore, evaluations on the Night-City dataset confirm its scalability and applicability to complex nighttime urban environments. NoctuDroneNet achieves state-of-the-art performance on NUI-Night, surpassing strong real-time baselines in both segmentation accuracy and speed. Qualitative analyses highlight its resilience to under-/over-exposure and small-object detection, underscoring its potential for real-world applications like UAV emergency landings under minimal illumination.