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机器视觉表面缺陷检测综述

汤勃,孔建益,伍世虔(武汉科技大学机械自动化学院, 武汉 430081)

摘 要
目的 工业产品的表面缺陷对产品的美观度、舒适度和使用性能等带来不良影响,所以生产企业对产品的表面缺陷进行检测以便及时发现并加以控制。机器视觉的检测方法可以很大程度上克服人工检测方法的抽检率低、准确性不高、实时性差、效率低、劳动强度大等弊端,在现代工业中得到越来越广泛的研究和应用。方法 以机器视觉表面缺陷检测为研究对象,在广泛调研相关文献和发展成果的基础上,对基于机器视觉在表面缺陷检测领域的应用进行了综述。分析了典型机器视觉表面缺陷检测系统的工作原理和基本结构,阐述了表面缺陷视觉检测的研究现状、现有视觉软件和硬件平台,综述了机器视觉检测所涉及到的图像预处理算法、图像分割算法、图像特征提取及其选择算法、图像识别等相关理论和算法研究,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
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)

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.
Keywords
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