On-Device Partial Learning Technique of Convolutional Neural Network for New Classes

Hur, Cheonghwan; Kang, Sanggil

doi:10.1007/s11265-020-01520-7

Cited by 2 publications

(2 citation statements)

References 41 publications

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“…It can realize the interaction and coordination among people, vehicles and roads, so as to effectively improve road traffic conditions and travel efficiency. The international automotive industry has recognized two categories of autonomous driving classification standards: National Highway Traffic Safety Administration and the Institute of Automotive Engineers, of which the second classification standard is more widely used ( Hur and Kang, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system. automotive industry has recognized two categories of autonomous driving classification standards: National Highway Traffic Safety Administration and the Institute of Automotive Engineers, of which the second classification standard is more widely used (Hur and Kang, 2020).…”

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confidence: 99%

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A lightweight network architecture for traffic sign recognition based on enhanced LeNet-5 network

An,

Yang,

Zhang

2024

Front. Neurosci.

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As an important part of the unmanned driving system, the detection and recognition of traffic sign need to have the characteristics of excellent recognition accuracy, fast execution speed and easy deployment. Researchers have applied the techniques of machine learning, deep learning and image processing to traffic sign recognition successfully. Considering the hardware conditions of the terminal equipment in the unmanned driving system, in this research work, the goal was to achieve a convolutional neural network (CNN) architecture that is lightweight and easily implemented for an embedded application and with excellent recognition accuracy and execution speed. As a classical CNN architecture, LeNet-5 network model was chosen to be improved, including image preprocessing, improving spatial pool convolutional neural network, optimizing neurons, optimizing activation function, etc. The test experiment of the improved network architecture was carried out on German Traffic Sign Recognition Benchmark (GTSRB) database. The experimental results show that the improved network architecture can obtain higher recognition accuracy in a short interference time, and the algorithm loss is significantly reduced with the progress of training. At the same time, compared with other lightweight network models, this network architecture gives a good recognition result, with a recognition accuracy of 97.53%. The network structure is simple, the algorithm complexity is low, and it is suitable for all kinds of terminal equipment, which can have a wider application in unmanned driving system.

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%