Traffic vehicle detection in UAV aerial images facilitates traffic analysis and holds significant implications for traffic management and optimization. While drone technology provides unique aerial advantages for ground object detection, traffic vehicle detection in aerial images remains challenging. Small-scale, low-resolution vehicles in cluttered environments with frequent occlusions degrade detection accuracy, compounded by sensitivity to lighting variations during imaging. Moreover, current high-performance detection models demand excessive computational resources and parameters, conflicting with drones' constrained edge computing capabilities and hindering practical deployment in traffic management systems. To address these challenges, this paper proposes a vehicle detection model based on feature optimization and precise convolution techniques. First, a plug-and-play feature optimization module is added to the backbone network's output, using multi-branch attribute convolution to capture detailed local contextual features for small object representation. Second, the precise subsampling convolution module addresses information loss in standard convolution, enhancing fine-grained feature learning. Lastly, the micro precision perception pyramid attention network strengthens feature interactions between deep and shallow layers, improving detection precision for small objects and intricate details. Experimental results show that the proposed model significantly improves accuracy over baseline models, achieving 91.4% in detecting small objects while slightly reducing parameters. Additionally, a lightweight version of the model outperforms benchmark models in various metrics. This progress highlights the effectiveness of the strategy and showcases the model's potential for real-world applications.