In the intelligent management of city traffic, the object detection of UAV vehicles plays a crucial role. Fixed-view cameras cannot comprehensively monitor urban road traffic, and using the flexibility and wide-area view of UAV can supplement the monitoring range of fixed cameras, improve intelligent traffic management and enhance urban traffic management efficiency. However, the limited resources of the UAV load platform and the unique viewpoint and flexibility of UAV imaging lead to image degradation and uneven sample distribution. To address these problems, a UAV vehicle obejct detection method based on TOOD was proposes in this study, where we use a lightweight network, RegNet-800M, as the feature extraction backbone network to reduce computational overhead and improve feature extraction. At the same time, we incorporate an attention mechanism module based on RegNet-800M to optimize the information processing capability of the model through both spatial and channel dimensions. The results show that the proposed method achieves an average accuracy of 55% and a detection speed of 31.6 FPS on the VisDone-2019 subset, and by balancing accuracy and speed, the method meets real-time standards and practical industry requirements.
UAV Vehicle Detection Method and Application Based on Improved TOOD
18.08.2023
4986450 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Vehicle detection method based on improved YOLOv3
IEEE | 2022
|Vehicle Light Detection Method Based on Improved CenterNet
Springer Verlag | 2022
|