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基于双目立体视觉和轻量化神经网络的交通标志分割和识别

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为了降低交通标志图像分割运算量,提出一种基于双目立体视觉和轻量化神经网络的交通标志分割和识别方法.使用已标定的双目立体视觉相机采集交通标志图像,并将其作为轻量化卷积神经网络的输入,通过卷积运算和池化运算提取交通标志的特征.在全连接层中,采用极限学习机和权值修正方法修正输出权值,从而得到交通标志的分割结果.实验结果表明,所提方法能够有效采集高精度的交通标志图像,并降低图像分割运算的复杂性,从而提高交通标志图像的应用性.
Traffic Sign Segmentation and Recognition Based on Binocular Stereo Vision and Lightweight Neural Network
In order to reduce the calculation of traffic sign image segmentation,this study proposes a traffic sign segmentation and recognition method based on binocular stereo vision and lightweight neural network.A calibrated binocular stereo vision camera is used to collect traffic sign images,which are used as inputs to a lightweight convolutional neural network.The fea-tures of traffic signs are extracted through convolution and pooling operations.In the fully connected layer,extreme learning machine and weight correction methods are used to correct the output weights,thereby obtaining the segmentation results of traffic signs.The experimental results show that this method can effectively collect high-precision traffic sign images and reduce the complexity of image segmentation operation,thereby improving the applicability of traffic sign images.

binocular stereo visionlightweighttraffic signoptimized segmentation methodextreme learning machine

孙静、刘晓燕

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新疆应用职业技术学院,传媒艺术系,新疆,伊犁 833200

新疆大学新疆历史文化旅游可持续发展重点实验室,新疆,乌鲁木齐 830046

新疆大学旅游学院,新疆,乌鲁木齐 830046

双目立体视觉 轻量化 交通标志 优化分割方法 极限学习机

新疆社科基金项目新疆历史文化旅游可持续发展重点实验室项目

22BJY029LY2022-09

2024

微型电脑应用
上海市微型电脑应用学会

微型电脑应用

CSTPCD
影响因子:0.359
ISSN:1007-757X
年,卷(期):2024.40(6)
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