An Embedded Deep Learning Based Traffic Advisory System

P. Raj, M. Kumar, Priyanka Dwivedi
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引用次数: 1

Abstract

This paper presents a deep learning-based traffic advisory system relying on the count of vehicles on road at a certain time as the parameter advising people to take alternative routes as per requirement. There are some techniques that have led to a better inference time for a deep learning model but they are computationally expensive. Although we can afford to carry out the expensive computation on the cloud, this could hamper the performance of the real time traffic advisory system. In the proposed method implementation of two different deep learning frameworks - You only look once (YOLOv3) and Tiny YOLOv3 - to clock a quicker inference time while maintaining a significant level of accuracy and scalability of the system. Towards the end, we have presented our detection results on Indian driving dataset for vehicle detection. A comparative analysis of 4 deep learning techniques namely YoloV5, Ssd, Faster RCNN and EfficientDet has been performed in terms of performance.
基于嵌入式深度学习的交通咨询系统
本文提出了一种基于深度学习的交通咨询系统,该系统以某一时刻道路上的车辆数量为参数,建议人们根据需要选择备选路线。有一些技术可以为深度学习模型带来更好的推理时间,但它们的计算成本很高。虽然我们可以负担得起在云上进行昂贵的计算,但这可能会影响实时交通咨询系统的性能。在提出的两种不同的深度学习框架的方法实现中——你只看一次(YOLOv3)和Tiny YOLOv3——在保持系统显著的准确性和可扩展性的同时,获得更快的推理时间。最后,我们展示了我们在印度驾驶数据集上的检测结果,用于车辆检测。对YoloV5、Ssd、Faster RCNN和EfficientDet 4种深度学习技术的性能进行了比较分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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