煤炭工程 ›› 2015, Vol. 47 ›› Issue (9): 114-116.doi: 10.11799/ce201509037

• 研究探讨 • 上一篇    下一篇

输送带纵向撕裂SOM检测方法

张伟1,刘海军1,李忠2   

  1. 1. 防灾科技学院灾害信息工程系
    2. 防灾科技学院
  • 收稿日期:2015-03-29 修回日期:2015-01-29 出版日期:2015-09-11 发布日期:2015-10-09
  • 通讯作者: 刘海军 E-mail:liuhaijun6741@163.com

SOM method for detecting longitudinal tearing of conveyor belt

  • Received:2015-03-29 Revised:2015-01-29 Online:2015-09-11 Published:2015-10-09

摘要: 为准确识别输送带纵向撕裂,将自组织特征映射(SOM)引入到输送带纵向撕裂检测中。输送带图像经过中值滤波预处理之后,提取其梯度方向直方图特征作为SOM网络的输入,采用卡方距离描述特征之间的相似性,建立了输送带图像纵向撕裂的SOM检测模型,详细介绍SOM训练过程,最后对文中算法进行了仿真实验。实验结果表明采用SOM网络识别输送带纵向撕裂具有良好的效果,为输送带自动识别拓宽了思路。

关键词: 输送带, 纵向撕裂, 自组织特征映射, 神经网络

Abstract: To recognize longitudinal tearing accurately, Self-Organizing feature Maps(SOM)was introduced to detect longitudinal tearing of conveyor belt. After median filtering, the histograms of oriented gradient of the conveyor belt images were extracted as the SOM’s input feature vectors.Then Chi-square distance was adopted to describe similarity of two different feature vectors. SOM detecting model was thus built and training process of the model was described in detail. Finally simulation experiment was done to test the effect of the mode. The result shows that the SOM network works well in recognizing longitudinal tearing of conveyor belt. The research in this paper brodens ways of longitudinal tearing detection.

Key words: conveyor belt, longitudinal tearing, Self-Organizing feature Maps, neural network

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