Effective and accurate detection of unmanned aerial vehicles (UAVs) is crucial for combating malicious UAV systems. However, adverse weather conditions, such as haze or low light, often degrade the quality of captured UAV images. Traditional target detection models applied to these low-quality UAV images usually suffer from decreased accuracy. To address this challenge, this paper proposes AW-YOLOv9, a UAV target detection method designed for adverse weather conditions. Specifically, an image enhancement processing (IEP) module with adaptive learning parameters is introduced to suppress weather-related interference in the images and restore hidden target information. The DP module embedded within YOLOv9 was designed to reduce the network’s parameter count while improving the detection speed. We established a dataset of UAV images under adverse weather conditions and utilized this dataset to validate the superiority of AW-YOLOv9. The experiments demonstrate that the proposed method achieves a 3.30% improvement in mean average precision compared to SOTA methods, effectively enhancing the detection accuracy of UAV targets under adverse weather conditions.