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- W4387366725 abstract "This paper presents a novel deep learning approach for the detection of traffic objects from drone-based imagery, focusing predominantly on the rapid and accurate detection of vehicles within road sections. The proposed method consists of two primary components: a road segmentation module and a vehicle detection network. The former leverages a residual unit with skip-connections to effectively extract road areas, while the latter employs a modified version of the YOLOv3 architecture, tailored for high-accuracy and high-speed vehicle detection. To address the issue of data imbalance, which is a pervasive challenge in drone images, this paper utilizes a range of data augmentation techniques to improve the robustness of the proposed model. Experimental results on the UAVDT and UAVid datasets exhibit that the proposed model attains a substantial boost in accuracy and inference speed of vehicle detection in comparison to the existing methods. These findings underscore the potential of the proposed approach for real-world traffic monitoring applications, where rapid and reliable vehicle detection is paramount." @default.
- W4387366725 created "2023-10-06" @default.
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- W4387366725 date "2023-01-01" @default.
- W4387366725 modified "2023-10-16" @default.
- W4387366725 title "Deep Neural Network-based Detection of Road Traffic Objects from Drone-Captured Imagery Focusing on Road Regions" @default.
- W4387366725 doi "https://doi.org/10.14569/ijacsa.2023.0140933" @default.
- W4387366725 hasPublicationYear "2023" @default.
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