基于增强神经网络算法的视觉mimo系统LED颜色检测

Partha Pratim Banik, Rappy Saha, Tae-Ho Kwon, Ki-Doo Kim
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引用次数: 0

摘要

LED颜色检测是视觉多输入多输出系统的重要组成部分。为了从LED阵列图像中确定传输符号,检测接收端LED的颜色是很重要的。本文提出了一种增强神经网络(boosting neural network, BNN)的训练算法来预测接收端LED的颜色。首先,我们取LED阵列的图像,利用LED检测算法对LED图像进行分割。在分割LED图像后,LED图像被调整为10 × 10的尺寸,即100像素。每个像素是每个RGB颜色通道的BNN模型的输入。为了研究每个彩色LED图像在低(565 lux)和强(2450 lux)环境光强度下的行为,我们训练了低和强环境光强度的BNN模型。最后,我们比较了BNN模型与回归分析模型在弱光和强光环境下的性能。在两种环境光强度下,我们获得了每个颜色通道更大的接近精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
LED Color Detection of Visual-MIMO System Using Boosting Neural Network Algorithm
LED color detection is a vital part in visual-MIMO system. For deciding transmitted symbols from an LED array image, it is important to detect the color of LED on receiver side. In this paper, we propose a training algorithm, called boosting neural network (BNN) to predict the color of LED on receiver side. First, we take the image of LED array and segment the LED image by using LED detection algorithm. After segmenting the LED image, the LED image is resized in 10 by 10 dimension that means 100 pixels. Each pixel is the input to the BNN model for each RGB color channel. For studying the behavior of each color LED image in low (565 lux) and strong (2450 lux) environmental light intensity, we train our BNN model for low and strong environmental light intensity. Finally, we compare the performance of our BNN model with the regression analysis model at low and strong environmental light intensity. We obtain greater closeness accuracy for each color channel at both environmental light intensities.
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