In order to facilitate the statistical analysis of turning traffic flow at intersections and enhance the detection speed and accuracy of turning vehicle flows, we propose a deep learning-based approach for detecting, tracking, and counting turning vehicle flows at urban intersections. Firstly, through comparative analysis, we select the lightweight and efficient YOLOv5s as the target detection framework. We acquire urban intersection traffic flow videos using unmanned aerial vehicle (UAV) aerial photography and construct a custom vehicle aerial image dataset. We employ pre-trained weights and the latest weight files to train and infer on our custom dataset. Model evaluation indicates that the vehicle detection model based on YOLOv5 exhibits high detection speed and accuracy: the model"s box_loss quickly decreases and stabilizes around 0.03, while mAP_0.5 swiftly rises and remains near 0.9. Subsequently, we integrate the DeepSORT model as the backend multi-vehicle tracking algorithm. We simplify vehicle trajectory extraction through coordinate transformations and conduct effectiveness judgments on trajectory lines. To address the phenomenon of abrupt changes in detection box corners, we propose a corner-to-centroid coordinate transformation to enhance the robustness of trajectory point coordinates. Additionally, we utilize a sixth-degree polynomial to fit vehicle trajectory lines and optimize rotation for trajectory lines that do not meet the function mapping requirements, ensuring proper fitting of all trajectories. Finally, based on pre-defined turning angle threshold criteria, we achieve turning vehicle detection and counting. To validate the effectiveness of our proposed method for turning traffic flow detection, we conduct vehicle detection experiments at a selected urban intersection. Comparative analysis between manual counting and our method"s detection results demonstrates an average detection accuracy of 92.9% across four directions, with a maximum accuracy of 95.7%, meeting conventional requirements for turning traffic flow detection in real-world intersection scenarios.