Nighttime perception remains a critical bottleneck for the autonomous operation of unmanned aerial vehicles （UAVs）. Under low-light conditions，weak and uneven illumination amplifies noise and glare，background interference becomes increasingly complex，and targets often appear small，low in contrast，or partially occluded. Meanwhile，UAV platforms impose strict constraints on onboard computation and power consumption，requiring detection and tracking methods to achieve balance between robustness and efficiency. This paper presents a comprehensive review and empirical study of the application of deep learning in UAV nighttime target detection and tracking by synthesizing methodological trends，experimental findings，and future research directions. We characterize the unique challenges of UAV nighttime vision in contrast to daytime scenarios and highlight four interrelated issues： 1） limited perceptual signals and variable illumination，leading to degraded pixel-level cues； 2） feature degradation and background interference caused by artificial light sources and shadows； 3） restricted onboard resources that make the deployment of large-scale models difficult； and 4） complex imaging conditions，where target scale and appearance fluctuate frequently. These constraints underscore the need for specialized algorithmic paradigms rather than direct transfer of daytime methods. From the perspective of UAV nighttime target detection，this study systematically reviews four major research directions. First，nighttime image enhancement methods employ dedicated restoration networks to improve brightness and contrast before detection. Although these approaches can effectively enhance visual quality，the two-stage pipeline may introduce artifacts and does not always optimally align with detector objectives. Second，domain adaptation methods leverage adversarial learning，style transfer，or feature alignment to bridge the day-night distribution gap. Such methods reduce the dependence on annotated nighttime data but remain sensitive to interdomain diversity and often require carefully designed pseudo-labeling or regularization. Third，multimodal perception fusion，especially RGB-infrared （IR） fusion，utilizes complementary thermal cues to mitigate illumination deficiencies. State-of-the-art fusion networks （e.g.，M²D-LIF） have demonstrated substantial mean average precision （mAP） improvements on UAV nighttime benchmarks，with some models achieving over 80% mAP@0.5 on the DroneVehicle-Night subset. Fourth，lightweight models （e.g.，CSPDarkNet-based backbones） focus on balancing accuracy and efficiency under limited computational budgets. Experiments indicate that backbone choice is critical for enabling real-time nighttime detection without a considerable accuracy loss. For nighttime target tracking，this study categorizes four representative paradigms. 1） Enhancement-before-tracking： low-light enhancers are used as a preprocessing stage （sometimes co-optimized） to improve input quality and thereby downstream tracker performance. 2） Domain adaptation： extending static feature alignment to temporal modeling，sometimes combined with synthetic nighttime sequences or foundation models，to enhance robustness. 3） Prompt learning： reframing day-night transfer as a fast adaptation task and leveraging small-scale prompts for efficient transfer with minimal parameter updates. 4） Curriculum learning： ordering training samples from easy to difficult （e.g.，progressively increasing darkness or motion blur） to stabilize training and improve performance under extreme degradation. 5） Multimodal fusion： we further analyze the interplay among these paradigms and the tension between restoration- and task-oriented objectives. Subsequently，commonly used evaluation metrics and benchmark datasets for nighttime and all-weather UAV detection and tracking are introduced，and a nighttime UAV vehicle detection dataset called DroneVehicle-Night is constructed. Two complementary empirical studies are conducted to support this survey. The first study，which is based on DroneVehicle-Night （derived from DroneVehicle），contains tens of thousands of paired RGB-IR annotated images covering urban roads，residential areas，and parking lots，with five classes of vehicles annotated. Experiments reveal that under identical training conditions，IR consistently outperforms RGB （e.g.，CSPDarkNet-based detectors achieve high mAP@0.5 in IR），and multimodal fusion methods，such as M²D-LIF，achieve over 83.9% mAP@0.5，underscoring the value of modality complementarity in nighttime detection. The second study evaluates the transferability of generic UAV detectors to the nighttime scenario. From VisDrone2019，we construct a paired subset （521 nighttime/513 daytime images）. Although generic detectors achieve 30%-45% mAP@0.5 on daytime data，their performance declines by 8%-12% at night，with small and low-contrast objects exhibiting the most severe degradation. These findings confirm that specialized nighttime and modality-aware methods are effective，and simple day-night transfer is insufficient. To further reflect the state of literature on nighttime tracking，this study summarizes the performance of 20 representative trackers on UAVDark135 and NAT2021 （results are cited from the original publications）. Among them，Mamba-based methods （e.g.，MambaNUT-Small and MambaTrack） and several domain adaptation methods （e.g.，DARTer and DCPT） consistently rank among the top，achieving robust AUC-accuracy trade-offs while maintaining reasonable frame rates. These results highlight the importance of efficient sequence modeling and domain-aware adaptation in nighttime tracking. On the basis of the survey and empirical evidence，several key conclusions are derived. First，sensor modality selection is critical. IR and RGB-IR fusion substantially enhance robustness to low-light and complex backgrounds，whereas RGB-only pipelines remain fragile. Second，backbone design strongly influences the accuracy-efficiency balance. Compared with heavy backbones，such as ResNet50，CSPDarkNet variants offer more attractive real-time trade-offs on embedded platforms. Third，aside from frame-by-frame strategies，explicit temporal modeling and sequence-oriented architectures are indispensable for robust nighttime tracking. Fourth，day-night transfer remains a major challenge，as evidenced by the VisDrone experiments that highlight the urgent need for specialized nighttime designs and datasets. Finally，four promising research directions are identified： 1） cross-modal vision-language fusion that leverages large-scale language/vision models to enhance multimodal alignment and interactive tracking； 2） construction of rich multimodal nighttime datasets that extend beyond urban scenes to cover wild，mountainous，and aquatic environments； 3） unsupervised and weakly supervised learning to reduce annotation costs and improve cross-domain robustness； and 4） lightweight architecture design. Aside from accuracy，UAV tasks demand real-time and efficient models. Future work may explore long-sequence modeling with Mamba in combination with neural architecture search，dynamic inference，and efficient operators to balance accuracy，latency，and energy. In conclusion，this survey and empirical study provide a clear roadmap toward robust UAV nighttime perception and practical deployment. All algorithms and datasets used in this work are publicly available at https://github.com/bsfsf/DroneVehicle-Night and https://doi.org/10.57760/sciencedb.32435, to facilitate future research.