计算机科学
无人机
稳健性(进化)
深度学习
人工智能
分割
残余物
推论
计算机视觉
目标检测
人工神经网络
实时计算
遗传学
生物
生物化学
化学
算法
基因
出处
期刊:International Journal of Advanced Computer Science and Applications
[The Science and Information Organization]
日期:2023-01-01
卷期号:14 (9)
被引量:1
标识
DOI:10.14569/ijacsa.2023.0140933
摘要
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.
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