基于卷积神经网络的道路使用者交通违法行为检测

2018 14th Symposium on Neural Networks and Applications (NEUREL) Pub Date : 2018-11-01 DOI:10.1109/NEUREL.2018.8586996

Jakub Špaňhel, Jakub Sochor, A. Makarov

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引用次数: 5

摘要

在本文中，我们探讨了在现实应用中基于神经网络的车辆和行人检测的实现。我们建议针对低功耗设备(如Nvidia Jetson平台)的功能对先前发布的方法进行更改。我们的实验评估表明，检测器能够在Jetson TX2上运行10.7 FPS，可以在实际应用中使用。

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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Detection of Traffic Violations of Road Users Based on Convolutional Neural Networks

In this paper, we explore the implementation of vehicle and pedestrian detection based on neural networks in a real-world application. We suggest changes to the previously published method with respect to capabilities of low-powered devices, such as Nvidia Jetson platform. Our experimental evaluation shows that detectors are capable of running 10.7 FPS on Jetson TX2 and can be used in real-world applications.

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2018 14th Symposium on Neural Networks and Applications (NEUREL)

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