Unmanned Aerial Vehicles (UAVs) offer high efficiency, low cost, and strong
mobility, making them well-suited for traffic vehicle detection. However, dense
targets, rapid scene changes, and small object sizes in aerial videos reduce
detection accuracy, which in turn affects the precision of speed extraction
algorithms. To address these issues, this paper proposes a speed extraction
method that integrates an improved You Only Look Once Version 11 (YOLOv11) with
the Deep Simple Online and Realtime Tracking (DeepSORT) algorithm. On the
detection side, several architectural enhancements are introduced. A Haar
wavelet-based HWD downsampling module preserves fine-grained details, a CSK2_m
multi-scale convolution block with a CCFM feature fusion structure strengthens
cross-scale representation, and an additional detection head at the P2 layer
improves the recall of tiny objects in complex scenes. Extensive experiments on
a hybrid dataset constructed from VisDrone2019 and a custom UAV dataset show
that the proposed model consistently improves detection across categories.
Notably, for two-wheel vehicles, precision increases by 9.0% and recall improves
by 4.6%, demonstrating clear advantages in small-object detection. Comparisons
with YOLOv5–YOLOv12 further confirm that the improved YOLOv11 achieves the best
overall accuracy, reaching an mAP@0.5:0.95 of 54.3% while maintaining real-time
inference capability at 65.9 FPS despite a moderate increase in parameters. In
addition, ablation studies verify that each module contributes to performance
gains, with HWD producing the largest improvement and the combined design
achieving the best results. Moreover, integration with DeepSORT significantly
enhances tracking, improving MOTA by 9.13% and reducing ID switches by nearly
two-thirds. Finally, three real-vehicle experiments using speed data from an
Integrated Inertial Navigation System (IINS) validate the method’s accuracy,
achieving a minimum mean squared error of 0.324. These results validate the
proposed method’s effectiveness and practicality in real-world UAV-based traffic
monitoring and speed estimation tasks.