Classification and recognition of 2D mechanical parts based on GA

LIU Yunhe; LI Su

doi:10.13873/J.1000-9787(2020)08-0148-04

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Classification and recognition of 2D mechanical parts based on GA

更新时间：2025-09-11

- Classification and recognition of 2D mechanical parts based on GA
- Vol. 39, Issue 8, Pages: 148-151(2020)
- 作者机构：
  
  山东大学机械工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2020)08-0148-04
  CLC： TP18;TP391.41
- Published：2020
- 稿件说明：
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[1]柳云鹤,李苏.基于遗传算法的2D机械零件分类识别[J].传感器与微系统,2020,39(08):148-151.

LIU Yunhe, LI Su. Classification and recognition of 2D mechanical parts based on GA[J]. 2020, 39(8): 148-151.
[1]柳云鹤,李苏.基于遗传算法的2D机械零件分类识别[J].传感器与微系统,2020,39(08):148-151. DOI： 10.13873/J.1000-9787(2020)08-0148-04.

LIU Yunhe, LI Su. Classification and recognition of 2D mechanical parts based on GA[J]. 2020, 39(8): 148-151. DOI： 10.13873/J.1000-9787(2020)08-0148-04.

摘要

提出了一种新的机器视觉零件分类方法。利用计算机图形

首先以3D模型建模机械零件

然后投影到x-y平面以生成一组2D图像;最后利用遗传算法(GA)的仿射变换采集图像和训练样本的模板比对进行识别。实验结果证明:本方法相比Hu不变矩方法具有较高的零件识别率。

Abstract

A new machine vision based mechanical parts classification method is presented. Firstly

computer graphics is used for modeling of mechanical parts by 3 D model. Then

project them to the x-y plane to generate a group of 2D images. Finally

affine transformation of genetic algorithm（ GA） is used to acquire images

and compare with template of training sample for identification. Experimental result shows that the proposed method has higher mechanical parts recognition rate compared with Hu invariant moment method.

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