Optimization Method for Traffic Parameter Extraction Based on YOLO from the Perspective of Drones

This article studies traffic target detection and parameter extraction from the perspective of drones. A YOLOv7-based improved traffic object detection algorithm and traffic parameter extraction method are proposed to address the problems of low detection accuracy for dense small targets and large extraction errors in traffic object detection tasks from the perspective of unmanned aerial vehicles. Add an attention mechanism after the original Backbone network to improve the feature extraction efficiency of the model; The EIoU position loss function is used to optimize the update efficiency of parameters. Maintaining a balance between detection speed and accuracy on the VisDrone dataset, the improved algorithm achieved a 2.20% increase in map compared to YOLOv7, demonstrating the effectiveness of the object detection algorithm and improving the extraction of traffic parameters in this scenario compared to general methods.