机器视觉表面缺陷检测综述

并对每种主要方法的基本思想、

特点和存在的局限性

进行了总结，对未来可能的发展方向进行展望。

结果

机器视觉 表面缺陷检测系统中，图像处理和分析算法是重要内容，算法各有优缺点和

其适应范围。 如何提高算法的准确性、实时性和鲁棒性，一直是研究者们努力的方向。

结论

机器视觉是对人类视觉的模拟， 机器视觉表面检测涉及众多学科和理论，如何使检测

进一步向自动化和智能化方向发展， 还需要更深入的研究。

关键词

机器视觉

;

表面缺陷

;

检测算法

;

图像处理

;

图像识别

Review of surface defect detection based on

machine vision

Tang Bo

,

Kong Jianyi

,

Wu Shiqian

School of Mechanical and Automation Engineering, Wuhan University of Science and Technology,

Wuhan 430081, China

Supported by:

Natural Science Foundation of Hubei Province, China(2016CFC752)

Abstract

Objective

Surface defects of industrial products exert adverse effects on

appearance, comfort, and service performance, and enterprises detect these

surface defects of products to control them in time. The manual detection method

is the traditional way of surface defect detection and is characterized by low

sampling rate, accuracy, and efficiency, poor real-time performance, high labor

intensity, and sensitivity to artificial experience. The detection method based on

machine vision can significantly overcome these disadvantages by manual

detection. Machine vision detection method can find a few problems existing in the

production process on the basis of the detection results to eliminate or reduce

product defects, prevent potential trade disputes, and maintain enterprise honor.

The detection method based on machine vision presents many achievements and

applications in metal, paper printing, textile, ceramic tile, glass, and wood surface

defect detection at home and abroad.

Method

The research and application of

surface defect detection based on machine vision are reviewed on the basis of

extensive research and the development results of relevant literature. The basic

structure and working principle of a typical surface defect detection system based

on machine vision are analyzed, and the research status and existing visual

software and hardware platforms of surface defect detection based on machine

vision are introduced. The relevant research of theory and image algorithm for

preprocessing, segmentation, feature extraction and optimization, and image

recognition are summarized. The main difficulties and development of visual

detection of surface defects are presented, and the development trend in this field

is concluded. The surface defect detection system based on machine vision

includes the following modules:image acquisition, image processing, image

analysis, data management, and man- machine interface. The image acquisition

module mainly consists of charge coupled device(CCD)cameras, optical lenses, and

light sources. The image processing module mainly involves image denoising,

image enhancement and restoration, defect detection, and object segmentation.

The image analysis module is mainly concerned with feature extraction, feature

selection, and image recognition. The data management and man-machine

interface module can display the defect type, position, shape, and size and can

carry out image storage, query, and statistics. Image preprocessing aims to reduce

noise and improve the quality of images and usually includes spatial and frequency

domain methods. In recent years, mathematical morphology and wavelet methods

are used in image denoising and obtain good results. Image segmentation means

dividing an image into several non- overlapping regions; each region possesses the

same or similar certain properties or characteristics, but the image features

between different regions present obvious difference. Existing image segmentation

methods are mainly divided into threshold-based, region-based, and edge-based

segmentations and specific theory methods. At present, new theories and methods

of other disciplines have been used in image segmentation. Image feature

extraction is the mapping from a high- dimensional image space to a

low- dimensional feature space. Image features can be divided into physical,

structural, and mathematical characteristics. A method that uses machine to

simulate human eye and nervous system as well as physical and structural features

does not exist; hence, mathematical characteristics are used to describe image

features in digital image processing. The commonly used image features at present

are mainly textural features, color features, and shape features. If the feature

dimension of the extracted image is too high, then redundant information will exist

in the extracted feature, thereby not only increasing the processing time but also

decreasing the accuracy of image processing. The correlation among the feature

dimensions of the extracted images can be decreased by decreasing the feature

dimension with feature selection or optimization as the processing method.

Feature selection method mainly includes principal component analysis,

independent component analysis, self- organizing map, genetic algorithm, and

Fisher, correlation analysis, relief, Tabu search, and nonlinear dimensionality

reduction methods. Theory for guiding the selection and optimization of features is

unavailable to date. Statistical and syntactic pattern recognitions are two basic

pattern recognition methods, and artificial neural networks and support vector

machines are the most widely used statistical pattern recognition

methods.

Result

Surface defect detection based on machine vision will be the main

direction in the future. Theoretical research and practical application of surface

defect detection based on machine vision have obtained encouraging results to

date, but some problems and difficulties remain to be solved. Image processing

and analysis algorithm, which include image preprocessing, segmentation of defect

regions, feature extraction and selection, and defect recognition and classification,

are important concepts. Many algorithms have appeared in each processing flow,

and each of which possesses its advantages and disadvantages and range of

adaptation. Researchers have focused mostly on improving the signal-to-noise

ratio, accuracy, efficiency, real-time performance, and robustness of the detection

system. Simulating the information processing function of the human brain to

construct an intelligent machine vision system still needs further theoretical

research.

Conclusion

Surface quality inspection based on machine vision has been

attracting much attention and application in modern automatic production. The

surface of machine vision detection is complex and involves many disciplines and

theories. Machine vision is the simulation of human vision, but the visual

mechanism of humans remains unclear. Expressing the visual process of humans by

computer is difficult. Therefore, the construction of machine vision inspection

system should be further improved through research of biological vision

mechanism. Accordingly, the detection will further develop to the direction of

automation and intelligence.

Key words

machine vision

;

surface defect

;

detection algorithm

;

image processing

;

images

recognition

0

引言

中国是一个制造大国，

每天都要生产大量的工业产品。

用户和生产企业对产品质量的要求越

来越高，除要求满足使用性能外，还要有良好的外观，即良好的表面质量。但是，在制造产< /p>

品的过程中，

表面缺陷的产生往往是不可避免的。

不同产品的表面缺陷有着不同的定义和类

型，一般而言表面缺陷是产品表面局部 物理或化学性质不均匀的区域，如金属表面的划痕、

斑点、孔洞，纸张表面的色差、压痕 ，玻璃等非金属表面的夹杂、破损、污点，等等。表面

缺陷不仅影响产品的美观和舒适度 ，

而且一般也会对其使用性能带来不良影响，

所以生产企

业对产品的表面缺陷检测非常重视，

以便及时发现，

< br>从而有效控制产品质量，

还可以根据检

测结果分析生产工 艺中存在的某些问题，

从而杜绝或减少缺陷品的产生，

同时防止 潜在的贸

易纠份，维护企业荣誉。

人 工检测是产品表面缺陷的传统检测方法，该方法抽检率低、

准确性不高、

实时性差、

效率

低、

劳动强度 大、

受人工经验和主观因素的影响大，

而基于机器视觉的检测方 法可以很大程

度上克服上述弊端。

美 国机器人工业协会

(RIA)

对机器视觉下的定义为：“机器视 觉是通过光学的装置和非接触

的传感器自动地接收和处理一个真实物体的图像，

以获得所需信息或用于控制机器人运动的

装置”

[

1

]

。

< /p>

机器视觉是一种无接触、

无损伤的自动检测技术，

是实现设备自动化、

智能化和精密控制的

有效手段，< /p>

具有安全可靠、

光谱响应范围宽、

可在恶 劣环境下长时间工作和生产效率高等突

出优点。机器视觉检测系统通过适当的光源和图像 传感器

(CCD

摄像机

)

获取产品的表面图

像，

利用相应的图像处理算法提取 图像的特征信息，

然后根据特征信息进行表面缺陷的定位、

识别 、分级等判别和统计、存储、查询等操作。

视觉表面缺陷检测 系统基本组成主要包括图像获取模块、

图像处理模块、

图像分析 模块、

数

据管理及人机接口模块。

< /p>

图像获取模块由

CCD

摄像机、光学镜头 、光源及其夹持装置等组成，其功能是完成产品表

面图像的采集。

在光源的照明下，

通过光学镜头将产品表面成像于相机传感器上，

光信号先

转换成电信号，进而转换成计算机能处理的数字信号。目前工业用相机主要 基于

CCD

或

CMOS(comple mentary metal oxide semiconductor)

芯片的相机 。

CCD

是目前机器视觉

最为常用的图 像传感器。

光源直接影响到图像的质量，

其作用是克服环境光干扰，

保证图像的稳定性，

获得对比度 尽

可能高的图像。

目前常用的光源有卤素灯、

< br>荧光灯和发光二级管

(LED)

。

LED

光源以体积小、

功耗低、响应速度快、发光单色性好、 可靠性高、光均匀稳定、易集成等优点获得了广泛的

应用。

< /p>

由光源构成的照明系统按其照射方法可分为明场照明与暗场照明、结构光照明与频闪光照< /p>

明。

明场与暗场主要描述相机与光源的位置关系，

明场照明指相机直接接收光源在目标上的

反射光，

一般 相机与光源异侧分布，

这种方式便于安装；

暗场照明指相机间接 接收光源在目

标上的散射光，

一般相机与光源同侧分布，

它的优点是能获得高对比度的图像。

结构光照明

是将光栅或线光源等投射到被测物上，根据它们产生的畸变，解调出被测物的

3

维信息。

频闪光照明是将高频率的光脉冲照射到物体上，摄像机拍摄要 求与光源同步。

图像处理模块主要涉及图像去噪、图像增强与 复原、缺陷的检测和目标分割。

由于现场环境、

CCD

图像光电转换、传输电路及电子元件都会使图像产生噪声，这些噪声< /p>

降低了图像的质量从而对图像的处理和分析带来不良影响，所以要对图像进行预处理以去< /p>

噪。

图像增强目是针对给定图像的应用场合，

有目的地强调图像的整体或局部特性，

将原来

不清晰的图像 变得清晰或强调某些感兴趣的特征，

扩大图像中不同物体特征之间的差别，

抑

制不感兴趣的特征，

使之改善图像质量、