YOLO-UTS: Lightweight YOLOv5 for UAV Traffic Monitoring and Surveillance

In the application of drone-based target detection, both the lightweighting of the model and its accuracy are crucial. Therefore, this paper aims to achieve model lightweighting and accuracy improvement through structural improvements to the YOLO model. To meet this objective, this work optimizes the structure of the existing model to address the challenge of operational limitations in small drones, which have restricted computational capacities. By integrating lightweight modules as the backbone of the model, it enhances the feasibility of deployment on devices with limited processing power. Furthermore, the introduction of a self-attention mechanism, which is placed in the Neck of the model improves the ability to prioritize critical regions within the image. This enhancement is crucial for accurately detecting overlapping or blurred objects encountered by the moving drone. Additionally, the modification of the Intersection over Union (IoU) metric, which now considers the aspect ratio and shape of the bounding boxes further refines the target detection capabilities, ensuring more precise and reliable object localization. Since drones do not remain at a constant height or position, the angles and distances of the vehicles which they capture are bound to change. This adjustment, which modifies how the IoU quantifies overlap, allows the IoU to more accurately quantify the degree of overlap of vehicle detection bounding boxes at different angles and distances, thereby providing more reliable target detection performance. Experimental results indicate that compared to existing YOLO models, our method achieves 30% reduction in model size and 1% improvement in accuracy.