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. 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. 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. 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.