基于生物启发的交通标志识别层次模型

2014 24th International Conference on Computer Theory and Applications (ICCTA) Pub Date : 2014-10-25 DOI:10.1109/ICCTA35431.2014.9521641

Amr Abdel Aziz, I. Imam, A. Shoukry

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引用次数: 0

摘要

在本研究中，使用Numenta实现“Nupic v1.7”对分层时间记忆(HTM)模型进行了研究，并使用标准基准数据调查了其在解决德国交通标志识别任务中的性能。它的性能与最先进的多列深度神经网络(MCDNN)的性能进行了比较，MCDNN已被证明在相同的任务中超过了人类的识别精度。

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Biologically Inspired Hierarchical Model for Traffic Sign Recognition

In this research, a study of the Hierarchical Temporal Memory (HTM) model using the Numenta Implementation “Nupic v1.7” and an investigation of its performance in solving the German traffic sign recognition task using standard benchmark data is conducted. Its performance is compared to that of a state of the art Multi-Column Deep Neural Network (MCDNN) that has been proved to exceed the human recognition accuracy for the same task.

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2014 24th International Conference on Computer Theory and Applications (ICCTA)

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