[1]赵旭阳,张延彬,王忠勇,等.基于SVM的声磁标签检测系统设计及其FPGA实现[J].郑州大学学报(工学版),2021,42(03):13.[doi:10.13705/j.issn.1671-6833.2021.03.003]
　Cheng Jian,An Hongbo,Guo Yinan,et al.Design of Acoustic Magnetic Label Detection System Based on SVM and FPGA Implementation[J].Journal of Zhengzhou University (Engineering Science),2021,42(03):13.[doi:10.13705/j.issn.1671-6833.2021.03.003]

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基于SVM的声磁标签检测系统设计及其FPGA实现()

分享到：

《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:

42

期数:

2021年03期

页码:

13

栏目:

出版日期:

2021-05-10

文章信息/Info

Title:

Design of Acoustic Magnetic Label Detection System Based on SVM and FPGA Implementation

作者:

赵旭阳;  张延彬;  王忠勇;  陈明亮

郑州大学信息工程学院;

Author(s):

Cheng Jian; An Hongbo; Guo Yinan; Ye Liang;

The Institute of Mining of the General Institute of Coal Sciences; School of Information and Control Engineering, China University of Mining and Technology; School of Electromechanical and Electromechanical Engineering, China University of Mining and Technology (Beijing);

关键词:

航拍图像; 吃水线区域; 轮廓统计筛选; 知识引导; 动态识别

Keywords:

EAS;  acoustic magnetic;  support vector machine;  FFT;  FPGA

DOI:

10.13705/j.issn.1671-6833.2021.03.003

文献标志码:

A

摘要:

针对强电磁干扰环境下,传统声磁EAS系统存在检测距离近,误报(漏报率高等缺点,提出了一种基于支持向量机,SVM的声磁EAS系统,并以芯片作为实现平台)通过快速傅里叶变换, FFT法提取标签(噪声信号频域特征,将其作为SVM算法训练样本)SVM训练模型对数据帧做有标签和无标签二分类,训练部分在MATLAB上进行分类计算部分在FPGA内部实现,采用三级并行计算结构,算法用时小于0.5MZ测试结果表明:有干扰环境下,硬标签最远检测距离可达1.45m,识别率最高可达99.4%软标签最远检测距离可达0.75m,识别率最高可达 98.9%)与传统声磁EAS系统相比,检测距离明显增大,识别率提高)该系统为复杂环境下EAS系统的稳定运行提供了一种解决方案)

Abstract:

In strong electromagnetic interference environment, traditional acoustic magnetic EAS system has some shortcomes, such as short detection distance, high rate of false alarm and miss alarm. In order to solve these problems, an acoustic magnetic EAS system based on support vector machine (SVM) is proposed. The algorithm is implemented on FPGA chip. The fast Fourier transform (FFT) method is used to extract the frequency characteristics of tag and noise signals, which are used as the training samples of SVM algorithm. SVM algorithm classifies data frames into labeled and unlabeled ones, and the training part is implemented in MATLAB. The classification calculation part is implemented in FPGA, which adopts three-level parallel computing structure, and the algorithm time is less than 0.5 ms. The test results showed that: Under the interference environment, the longest detection distance of hard tag can reach 1.45 m, and the recognition rate can reach 99.4%. The longest detection distance of soft tag can reach 0.75 m, and the recognition rate can reach 98.9%. Compared with the traditional acoustic magnetic EAS system, this system can significantly increase the detection distance, improve the recognition rate, and provide a solution for the stable operation of EAS system in complex environment.

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