VFD-YOLO: A Novel Method for Vehicle Detection from a Drone Perspective

Abstract

Vehicle detection using drone-based imagery holds significant practical value in applications such as intelligent traffic management, urban situational awareness, and emergency response coordination. It is among the core technologies for achieving coordinated air–ground intelligent monitoring. However, when viewed from the high-altitude perspective of a drone, vehicle images suffer from issues such as complex backgrounds, blurriness, and low light, severely limiting the accuracy of model detection. Therefore, we propose a vehicle detection model for unmanned aerial vehicles based on YOLOv13 (VFD-YOLO). First, to address the issues of blurriness and low illumination in drone images, we designed a convolutional structure VFD-C3k2 specifically for vehicle feature extraction. It is a multi-scale feature-embedding structure based on the existing HLFD structure. We decomposed the vehicle image features into high-frequency details such as edges and textures, as well as low-frequency structural information such as global contours. Through differentiated processing, we enhanced the image restoration and detail extraction capabilities, and improved the adaptability of the model to different types of images. Afterward, to address the strong background interference from vehicle targets in drone imagery, we employed the CASAB channel and spatial attention module. This module enhances the weights of key vehicle feature channels through channel attention, focuses on the target area of the vehicle through spatial attention, effectively suppresses background noise, and strengthens the model’s ability to focus on and extract significant target features. The experimental results show that on the DroneVehicle dataset, the precision, recall rate, mAP@0.5 and mAP@0.5:0.95 of our proposed VFD-YOLO model reach 75.0%, 75.6%, 79%, and 54.7%, respectively, which represent improvements of 1.6%, 1.9%, 1.7%, and 2.4% compared with those of the baseline YOLOv13 model. In summary, the model we propose exhibits superior detection performance and can better meet the needs of practical scenarios such as intelligent transportation and urban surveillance.