UAV-YOLO Model v1

This research is based on the hypothesis that object detection performance in UAV-based traffic management systems can be significantly improved by redesigning the YOLOv5s architecture to better address the unique challenges of aerial imagery. These challenges include extremely small object sizes, large scale variation within a single frame, frequent occlusion, and limited onboard computational resources. The study proposes that integrating lightweight convolutional modules, Transformer-based attention mechanisms, enhanced multi-scale feature fusion, and an additional detection head into YOLOv5s can improve detection accuracy particularly for small and densely distributed objects while maintaining real-time inference capability suitable for UAV deployment. To evaluate this hypothesis, a dataset consisting of UAV-captured aerial images and video frames was employed. The data represents realistic traffic environments such as highways, intersections, and urban road networks, containing vehicles, pedestrians, bicycles, and other road users observed from varying altitudes and camera angles. The dataset was sourced from publicly available UAV traffic datasets and further supplemented with additional annotated aerial imagery to ensure diversity in object categories, environmental conditions, object densities, and scale variations. All images were manually annotated using bounding boxes following standard object detection protocols, with particular attention given to small and partially occluded objects that are critical for evaluating UAV-based detection performance.