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- W2946843211 abstract "In order to improve the efficiency of urban traffic operation, this paper combines deep learning technology and UAV photography of drones. With the algorithm in this paper, we can classify traffic area and static area in high quality and speed. We make a test in Fengtai District of Beijing to conduct regional traffic identification research on the traffic content of the area. The primary identification area includes the vehicle travel area and coordinates the pedestrian area in a coordinated manner. The main research result of this algorithm is to propose a key frame extraction scheme for UAV image and then combine it with the application of Mask R-CNN in high-altitude image to identify the ground area. The experimental results are similar to the same algorithm (refer to FCNs for this article). Comparative benchmarks) have obvious advantages of high speed and high accuracy, which are of great help to traffic safety and urban planning." @default.
- W2946843211 created "2019-06-07" @default.
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- W2946843211 date "2019-01-01" @default.
- W2946843211 modified "2023-09-25" @default.
- W2946843211 title "Research on UAV Image Classification Based on Deep Learning" @default.
- W2946843211 doi "https://doi.org/10.5220/0008098701480152" @default.
- W2946843211 hasPublicationYear "2019" @default.
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