透明件表面缺陷的机器视觉检测综述

明五一; 贾豪杰; 何文斌; 魏爱云

doi:10.13433/j.cnki.1003-8728.20190331

透明件表面缺陷的机器视觉检测综述

doi: 10.13433/j.cnki.1003-8728.20190331

明五一^1,,
贾豪杰¹,
何文斌^1, ,,
魏爱云^{1, 2}

1.
郑州轻工业大学机电工程学院, 郑州 450002
2.
广东华中科技大学工业技术研究院广东省制造装备数字化重点实验室, 广东东莞 523808

基金项目:

河南省自然科学基金项目 182300410170

河南省自然科学基金项目 182300410215

河南省高校科技创新团队项目 182300410215

广东省制造装备数字化重点实验室开放项目 2017B030314146

详细信息

作者简介:
明五一(1981-), 副教授/高级工程师, 博士, 研究方向为难加工材料数字化工艺与装备, mingwuyi@163.com

通讯作者:
何文斌, 教授, 博士, hwb@zzuli.edu.cn

中图分类号: F416.67
计量
- 文章访问数: 180
- HTML全文浏览量: 703
- PDF下载量: 91
- 被引次数: 0
出版历程
- 收稿日期: 2019-08-24
- 刊出日期: 2021-01-01

Detecting Surface Defects of Transparent Parts with Computer Vision

MING Wuyi^1
,,
JIA Haojie¹,
HE Wenbin^{1
, ,},
WEI Aiyun^{1, 2}

1.
Zhengzhou University of Light Industry, Mechanical and Electrical Engineering, Zhengzhou 450002, China
2.
Guangdong Provincial Key Laboratory of Manufacturing Equipment Digitization, Guangdong HUST Industrial Technology Research Institute, Dongguan 523808, Guangdong, China

摘要

摘要: 随着现代科技的发展，透明件几乎运用于各个行业并起着不可或缺的作用，透明件表面质量是衡量其合格与否的一个重要指标，同时机器视觉技术因具有速度快、精度高、成本低、稳定性好等优点被广泛用于透明件表面缺陷的检测。本文主要从图像采集、图像处理和缺陷识别几个环节来介绍透明件表面缺陷的检测，对检测系统的类型，采集图像的处理方法以及实验数据的整理进行深入的研究，结合图像特征与深度学习方法对透明件表面缺陷进行归类，探讨机器视觉检测透明件技术发展近状及现存问题。进一步，本文阐述了机器视觉检测透明件的最新进展，并对未来可能发展趋势进行预测，为后续研究工作提供基础理论参考。
- 机器视觉 /
- 透明件 /
- 表面缺陷 /
- 检测系统 /
- 图像处理
Abstract: The surface quality of transparent parts is an important indicator to measure whether they are qualified or not. Computer vision technology is widely used in the detection of surface defects of transparent parts because of its advantages such as high speed, high precision, low cost and good stability. This paper mainly introduces the detection of the surface defects of transparent parts with image acquisition, image processing and defect recognition. This paper uses the image feature method and the deep learning method to classify the surface defects of transparent parts and discusses the recent developments and existing problems of computer vision technology for detecting surface defects of transparent parts.
- omputer vision /
- transparent parts /
- surface defect /
- detection system /
- image processing

HTML全文

图 1 机器视觉检测系统原理图

下载: 全尺寸图片幻灯片

图 2 机器视觉表面缺陷检测系统图

下载: 全尺寸图片幻灯片

图 3 图像采集照明系统

下载: 全尺寸图片幻灯片

图 4 Halcon软件对玻璃瓶样品进行缺陷分析^[38]

下载: 全尺寸图片幻灯片

表 1 各类常用光源的性质

名称	耗电量/W	亮度	响应速度	协调控制	发热量	可靠性	使用寿命/h
钨丝灯	15~200	较量	慢	高	高	低	3 000
卤素灯	100	亮	慢	高	极高	低	3 000
荧光灯	50~100	较量	慢	较高	较高	较好	5 000
镁氖灯	16	较量	较快	高	较高	较好	6 000
LED灯	极低	多个LED达到很亮	快	多种形式	极低	较高	10 000

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表 2 图像分割在透明件表面缺陷检测中的应用情况

具体方法	研究对象	特征	文献来源
迭代法	透明零件	适用于图像灰度直方图灰度高的像素数量较少	[8]
	液晶显示屏		[20]
大津法(OTSU)	玻璃	图像灰度直方图中的波谷很平、很宽, 并且受噪声干扰严重难以寻找最佳阈值	[21]
	啤酒瓶		[11]
二次大津法	浮法玻璃	适用于低亮度环境和低对比度图像	[22]
双峰法	塑料制品	操作简单但灰度直方图必须呈明显的双峰状	[18]
最大熵值法	玻璃瓶	图像受噪声干扰较小且结构简单	[23]

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表 3 图像边缘提取在透明件缺陷检测的应用情况

具体方法	研究对象	特征	文献来源
	塑料制品		[19]
Canny	缎纹玻璃	操作简单但易把噪点误判为边界	[25]
	医用玻璃管		[8]
Sobel	玻璃	对灰度渐变低噪声图像效果较好	[26]
Robert	家用玻璃制品	定位精度高且对噪声敏感	[9]
水平集方法	液晶显示屏	适用于具有重复纹理背景的图像	[27]
各向异性扩散	玻璃基板	图像细节可以保存	[28]

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