<p>Object detection in Unmanned Aerial Vehicle (UAV) imagery, a specialized subfield of visual perception, becomes substantially more challenging under nighttime conditions. In addition to inherent issues such as multi-scale targets and densely distributed small objects, low illumination reduces image visibility and affects detection accuracy. To address these challenges, we propose MFLDet, an end-to-end framework that integrates low-light image enhancement with object detection, specifically designed for nighttime UAV imagery. Unlike existing approaches, this work is the first to embed a low-light enhancement strategy that first restores brightness and then performs denoising within an end-to-end detection framework for nighttime UAV scenarios. To implement this strategy, the framework incorporates a Low-Light Enhancement Network (LNet) to generate informative enhancement features, which are fused with detection features at the feature level. Beyond the enhancement stage, a Local and Global Feature Enhancement (LGFE) module enhances attention to object regions while suppressing background interference. Using compact proxy representations, LGFE achieves a balance between computational efficiency and long-range dependency modeling. A Multi-Scale Feature Fusion Module (MSFM) further enriches semantic representations to improve small-object perception in complex nighttime scenes. Extensive experiments on curated nighttime subsets of the VisDrone and DroneVehicle datasets demonstrate that MFLDet outperforms state-of-the-art methods, achieving an mAP50 of 46.9% on VisDrone (night) and 72.7% on DroneVehicle (night), surpassing previous best approaches by 1.2% and 5.3%, respectively. These results confirm the robustness and accuracy of MFLDet for UAV-based nighttime object detection.</p>