基于带钢表面缺陷检测系统的视觉研究进展外文文献翻译、中英文翻译
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基于带钢表面缺陷检测系统的视觉研究进展文 摘钢是大量的材料选择和非常多元化的工业应用。表面质量以及其他属性是最重要的质量参数, 特别是对于扁钢产品。传统手工表面检验程序非常不足,确保保证表面质量免费。为了确保客户的严格要求,自动建立钢铁表面检查技术在过去的二十年被发现是非常有效和流行的。考虑到它的重要性,本文试图通过对钢表面缺陷检测和分类建立第一个正式审查技术发展水平。可以看出大多数的研究工作一直在进行冷钢带表面，是客户需求中最敏感的。对热轧带材和棒材/棒表面缺陷检测工作也显示出增长在过去的10年。审查涉及总体方面的钢表面缺陷自动检测和分类系统使用应用技术。人们的注意也被吸引到报道成功率以及实时操作方面等有关的问题。关键词:钢铁表面检验;缺陷检测;缺陷分类;自动视觉检测审查1简介:钢铁表面和其自动检查的重要性钢铁可能是最重要的金属的量子和各种各样的使用。钢铁对工业社会的发展作出了巨大贡献。事实上,钢铁消费被认为是一个标准来判断一个国家的发展状况。根据世界钢铁协会,在2013年粗钢产量为15.82亿吨(吨),比其他所有的生产图金属放在一起。今天,有超过3500等级的钢的扁钢产品贸易约占50%。一个集成的钢铁制造工厂生产铁矿石在高炉铁水、焦炭、烧结矿和通量作为输入。液态铁转化为钢液与指定由中小学炼钢流程组成。钢液不断铸石板和坯料。板是典型的矩形截面的尺寸板1600 -毫米宽,250 -毫米厚,12000 -毫米长。坯料的方形截面通常大约150150毫米和12000毫米长。板是随后条,然后冷卷成热。坯料轧制成各种维度。一个简化炼钢过程的流程图如图1所示。钢材的表面质量的重要性,冷轧钢板的下尤其认为重要性自1980年代主要是由于要求汽车汽车制造商。在课程的时候,热带材表面质量,近年来,结构性产品的表面质量如棒/酒吧认为重要。传统上,扁钢产品的表面质量,在线圈形式,判断手动通过削减约30米的无规卷曲在一个批处理和检查由一个专家。通常,在手动检查,检查表面是钢铁表面产生约占总数的0.05%。在冷轧机复杂,运营商有时驻扎检查成品的在线缺陷。然而,由于线路速度高、疲劳和其他不利因素,审查过程并不令人满意。因此,手动检查过程不足以保证钢材表面没有缺陷和合理程度的信心,当然,需要自动表面检测做了。在重大的发展1,九个钢铁公司和我们三个铝公司在1980年代早期开始一个研究项目建立钢铁表面年检与两个商业合作组织。一个原型系统是建立在1987年在几个钢铁厂和测试。与此同时,欧洲企业也开始工作。因此,从1980年代以后的一半,研究工作表面检查钢铁产品开始。今天,建立自动表面检测系统(网络)是由许多著名公司。自2006年以来,每年国际表面检验峰会(ISIS)是由组成的一个财团等等。钢铁产品的技术应用自动检查,尽管不是100%准确已经成熟了。2复杂的钢铁表面检查自动化钢材表面的实时检测面临着一系列的挑战。困难可以列举如下：危险场所。为检验设备安装地点（照明系统，摄像机和一些信号处理设备），特别是，热辊的米尔斯是很危险的。环境温度高，粉尘，石油的存在，水液滴和水蒸气是很常见的。此外，该照明系统和相机需要防冲击和振动。此外，重型设备和场地在日常的感动，每周和每年的维护。这些因素都需要适当的物理和环境保护措施，现场设备的使用。运行速度。在日常生产中，表面的运行速度进行检查一般是高。扁平钢产品，在滚动的速度，在检查设备的操作，通常是20米/秒长的产品，特别是线材，速度是225英里/小时的高（100米/秒） 2 。在这样的高速实时操作需要特殊的图像处理设备和软件的执行时间小。在不同的钢制品表面缺陷的品种有报道是非常高的 3 。例如，出版社 4 已经把表面缺陷热轧产品九大类29个亚类。这些缺陷都不受任何标准。因此，他们的特点和分类，并从机厂商有所不同。此外，由于在生产过程中的变化，这些缺陷表现的变化。大量的摄像机。扁平钢产品，两套检测系统-一个顶部和底部表面需要另一个。这些反过来一般由3至4的相机盖带的整个宽度。长的钢产品，多个摄像机位于外周以保证整个表面覆盖。例如，一个圆形产品，至少三的相机同时使用五台摄像机已在文献 5 报道用。因此，对图像采集和实时处理是一项艰巨的任务。3现有的文献综述多年来,许多审查论文(6 - 12)表面缺陷检测的诸多方面的报告。各个方面和纹理分析方法一直在审查(13、14)。两个相对最近审查本拍纸簿6、7。表面缺陷检测使用纹理分析技术的进步已经被谢绝6覆盖处理主要应用于纺织品、砖和木头。 7了非常全面的研究工作在织物表面缺陷检测和提供一些有价值的结论。审查论文特别是纹理缺陷和面料也提到钢铁表面缺陷分类识别技术可以应用的地方。值得一提的是,早在1982年,11个文件是列在“检验在金属加工行业”审查由下巴和哈洛12。冈萨雷斯和森林15提供了一个出色的理论背景图像处理的各个方面,而理论依据神经网络分类由充分浸16。然而,作者不能找到任何审查的研究工作领域的钢表面缺陷检测和分类。因此,本文尝试从学术界巩固已发表的文献,钢铁行业和制造商的主题自动缺陷检测和分类的钢铁表面。4可用性研究的出版物上自动建立钢铁表面检查发表文献的可用性钢表面主要由各学术机构的研究工作,钢铁厂/钢铁厂研究单位和表面检测设备制造商。许多研究工作已经联合发表的学术/科研院所和钢铁厂表明良好的合作伙伴关系。在过去的10年中,相当比例的出版工作在钢铁表面系统来自中国。这是符合中国钢铁制造业占主导地位的存在。已发表的一些论文报道的研究工作主要集中在缺陷分类方面实现商业采购系统。而整体系统和他们的利益被著名致力于良好的文档记录,细节的缺陷检测和分类并不详细,可能由于知识产权问题。5钢表面的类别类型的钢表面缺陷检测.研究:板、棒、板、热地带,寒冷的地带,杆/酒吧。它们覆盖大部分钢作为材料的应用。冷,和后期,杆/酒吧得到更多研究者的关注。这主要是解释说,大比例的这些产品是成品,客户的质量要求越来越严格。广泛、钢铁表面可以在平面和长产品分类(图2)。平板产品表面可以进一步被分类如下:板/坯:都是由连铸过程钢液和有一些相似性对表面和内部条件。表面覆盖规模越来越模糊。板是由加热一块约为1250C和随后滚。表面氧化,甚至相对对板。热条是由加热一块约为1250C和滚动滚动站在多个减少厚度所需的值。带钢表面氧化。然而,由于轧制力高,大大减少了热表面粒度带板。冷带是由在冷轧机轧制热条酸洗过程(去除表面氧化层和清洁)。因此,冷条的表面没有氧化,表面很光滑由于很高的轧制力用于冷变形过程。-涂带(镀锌、镀锡)/完成不锈钢带表面在本质上是高度反光的。长的产品表面可以进一步被分类如下:棒/禁止生产钢坯热轧过程,及其表面氧化。进一步,表面也不平坦,因此,反射角向外围从而产生不均匀的图像强度不同。等长的产品角度、通道重型等生产从坯/开花。他们是复杂的截面和需要特殊照明和相机的安排。6钢材的表面缺陷列表有一个大的各种表面缺陷对不同钢产品。此外,没有统一标准的缺陷。也有大型国际集团相似性和内部集团多元化的17各种类的缺陷,使得缺陷分类困难。缺陷目录发布的是一家现代化的、德国4作为事实上的标准。试图列出了一些主要已被称为文学的表面缺陷检测和分类在过去的两年半。缺陷相对于上述类别的钢铁表面。7自动表面检测系统硬件结构的关键元素图3显示了网络多媒体的基本硬件结构。它由一个或多个光源,一个或多个相机(亮视场或亮和暗视野),高速图像处理器、服务器和操作员界面。7.1图像采集表面获得满意的图像质量,照亮表面充分和统一。事实上,高质量的照明减少图像处理的计算负担。两种类型的照明技术可用于金属表面:强度和范围成像。(在18到22)讨论了照明系统的各个方面,对金属表面。研究成像系统的冷带已经被很好地记录下来了23。成像范围提供了高度的信息从而使3 d缺陷突出。成像范围不是竞争强度成像。一般来说,使用范围成像在钢表面缺陷的研究并不多见。强度成像的主要是两种类型:明亮的场和暗场。在明亮的照明领域,传感器捕捉最直接的反射光。表面看起来明亮,而缺陷特性显得更黑。在暗场照明,入射光线的角度表面法向量是非常大的。这个结果在一个黑暗的表面,但有些缺陷图像中出现明亮。暗视野观点需要更强烈的照明。约8倍而亮视场照明要求报道21。不幸的是,所有表面缺陷不会出现在明亮的领域或仅在暗视野。有很多的例子使用两套摄像头覆盖视图的字段(24 - 26日)。使用20电荷耦合器件(CCD)区域扫描相机用来捕捉表面图像的双方热轧条使用明视场和暗场模式已报告在中国一家钢铁工厂24。然而,考虑到维护问题和系统的复杂性,大多数的系统将相机在明视场和暗场之间的位置。7.2光源提供所需的光源均匀光尽可能。虽然照明要求特别安排的光电源27,提供统一的强度是不可能由于使用多个光源在大多数情况下。图4显示了入射光强度的变化对钢的表面使用两个至强灯28。类型的光源用于一般是:广泛荧光管、卤素、至强和领导。7.3型摄像机一般来说,使用高分辨率CCD相机。使用线扫描和区域扫描相机已经在文献报道。线扫描相机已被广泛使用,因为它更容易意识到一个强大的,甚至照明区域表面进行检查。线扫描相机的缺点是,他们不能生成一个完整的形象,需要一个外部硬件建立图像从多个线扫描7。大部分的自动表面检测系统制造商使用线扫描。区域扫描相机、运输编码器的使用是可选的,检查决议在两个方向上独立于对象(web)的速度。然而,尽管使用区域扫描相机,甚至需要特别注意确保照明面积的扫描尽可能。高分辨率摄像机也用作免费系统30。7.4摄像头和图像分辨率相机分辨率。线扫描相机分辨率通常是1024(交叉网络)1(网络)和20481像素。31报道使用40961像素的相机。制造商通常使用1024 / 2048/40961像素。区域扫描:已报告600400像素的32。在33,40961000像素用于板。图像分辨率。各种尺寸的图像的决议已报告31日24日,26日,33岁,34)。跨web从0.17毫米到1毫米,而报道决议从0.25到1.25毫米不等。7.5图像处理计算机硬件CCD摄像机记录了一个图像转移到某种形式的快速、并行处理系统专用的相机和靠近它24。确保实时操作的并行处理系统处理大量图像数据并选择感兴趣的和存储区域(roi)。并行处理系统可能是相机本身的一部分,或FPGA与特殊硬件处理器或通用处理器。这一部分系统至关重要的实时操作以及缺陷检测和分类的准确性。此后,与大型备份服务器内存用于进一步的处理和操作的接口。8缺陷检测和分类的方法列表各种方法/技术用于钢铁表面的缺陷检测和分类列出。表1显示了不同的方法的列表.检测相对于获得本研究的引用。类型的钢表面也被提到在桌子上。技术后可能广泛统计,形态,空间域滤波、频域分析、联合空间/局部分析和分形模型。空间域滤波、形态学操作和关节空间y域过滤被发现广泛用于所有类型的表面。表面检查的最终目标是使用分类归类指定类缺陷。作为一个过程、分类开始后缺陷局部分割。通常在这个阶段,很多功能是提取的区域。理想情况下,不同的组合匹配所需的这些特性是独特和不同类型的缺陷。匹配通常是使用学习方法如神经网络反向传播(NN-BP)、支持向量机(SVM)等。自适应学习的两种类型:1)监督的网络提供了大量已知的典型输入。此后,网络产生已知输出尽可能基于培训。b)在无监督学习,网络需要各种输入之间的关系没有被告知。然而,钢表面缺陷展览大型国际集团相似性和内部多样性。因此,找到合适的特性和识别分类器计算成本较低是主要的研究领域。表2显示了分类方法的列表引用和类型的表面。结论本文处理的自动化检测方法对钢铁表面使用图像处理技术。审查出版物在两年半的提供了一个了解发生在这一领域的最新进展。主要观察如下:a)由于恶劣的环境,需要特别注意照明和成像系统的设计。钢铁表面图像据报道,由于表面氧化皮含有大量的噪声,振动,异常/变量照明,存在伪缺陷等表面缺陷的不规则形状和他们的类型和特征发生显著的变化从一个工厂到另一个。特征的缺陷也依赖生产条件。b)已发表的文献表明,相对重视为冷轧带钢表面缺陷的检测。最近,注意力也集中在表面的热条和酒吧/棒。多种技术,无论是在空间和频率域,已经申请了缺陷检测。通常,组合的几个技术提供了有用的结果。关于缺陷分类, 某种形式的神经网络或基于支持向量机技术找到的使用。实时操作的自动化检查系统通常需要非常快的处理图像的轧机速度通常是非常高的平面和钢产品。这需要每个摄像机的专用硬件系统具有并行处理能力。c)不谨慎的比较不同技术的结果是由于缺乏共同的标准对图像和实验方法。这个问题是进一步复杂由于缺乏标准定义的缺陷类型。d)商业化生产的自动化应用检查系统网络材料已达到高水平的成熟。然而,他们需要得到适当的调整为特定的应用程序。也连续设计师和用户之间的协作是必要的安装系统适应新品种/特征的缺陷在同一安装位置。引用1。杜邦F、C Odet、米箱、优化的缺陷识别的扁钢产品成本矩阵理论。取决于国际30(1),3 - 10(1997)。7月的12日访问2。贾庆林,YL Murphey,J施正荣,T,表面缺陷检测的智能实时视觉系统(IEEE-Proceedings第17届国际会议在模式识别,2004),页2 53。Sharifzadeh Alirezaee,R Amirfattahi,距首都普里什蒂纳,检测钢缺陷使用图像处理算法(IEEE国际会议,2008),页125 1274。C公园,SC赢了,一个自动化web表面热线材使用非抽取小波变换和支持向量机(工业电子、IECON 09年,IEEE的35年会上,2009),页2411 24155。X谢,审查使用纹理表面缺陷检测的最新进展分析技术。电子。列托人。视觉形象肛门。7(3),1-22(2008)6。库马尔,织物疵点检测:一项调查。IEEE反式。印第安纳州。电子。55(1),348 - 363(2008)7。M Shirvaikar,自动视觉检测的趋势。j . Proc实时图像。1(1),41-43(2006)8。Y李,来自G培华学院、自由表面检查技术最先进的审查。爱思唯尔、计算机辅助Des。36岁,1395 - 1417(2004) E V I E WOpen AccessReview of vision-based steel surface inspection systems AbstractSteel is the material of choice for a large number and very diverse industrial applications. Surface qualities along with other properties are the most important quality parameters, particularly for flat-rolled steel products. Traditional manual surface inspection procedures are awfully inadequate to ensure guaranteed quality-free surface. To ensure stringent requirements of customers, automated vision-based steel surface inspection techniques have been found to be very effective and popular during the last two decades. Considering its importance, this paper attempts to make the first formal review of state-of-art of vision-based defect detection and classification of steel surfaces as they are produced from steel mills. It is observed that majority of research work has been undertaken for cold steel strip surfaces which is most sensitive to customers requirements. Work on surface defect detection of hot strips and bars/rods has also shown signs of increase during the last 10 years. The review covers overall aspects of automatic steel surface defect detection and classification systems using vision-based techniques. Attentions have also been drawn to reported success rates along with issues related to real-time operational aspects.Keywords: Steel surface inspection; Defect detection; Defect classification; Automated visual inspectionReview1. Introduction: importance of steel surface and its automated inspectionSteel is probably the most important of all metals in terms of its quantum and variety of use. Steel has contributed immensely towards the development of industrial society. In fact, consumption of steel is considered to be one of the yardsticks to judge the developmental status of a country. As per World Steel Association, production of crude steel during 2013 was 1,582 million tons (Mt), which is more than production figure of all other metals put together. Today, there are more than 3,500 grades of steel available out of which trade in flat steel products accounts for about 50%.An integrated iron and steel making plant produces liquid iron in blast furnace with iron ore, coke, sinter and flux as input. Liquid iron is converted to liquid steel with specified constituent by primary and secondary steel making processes. Liquid steel is continuously cast into slabs and billets. Slabs are of rectangular cross-sectionwith dimension of a typical slab being 1,600-mm-wide, 250-mm-thick and 12,000-mm-long. Billets are normally of square cross-section of about 150 150 mm and about 12,000-mm-long. Slabs are subsequently rolled into hot strips and then to cold strips. Billets are rolled into struc-tural of various dimensions. A simplified flow chart of steel making processes is shown in Figure 1.Importance of surface quality of steel products, par-ticularly that of cold-rolled steel assumed importance since 1980s primarily due to demands from automotive car makers. In course of time, hot strip surface quality, and in recent times, surface quality of structural products like rods/bars have assumed significant importance.Traditionally, surface quality of flat steel products, which are in coil form, is judged manually by cutting about 30 m of a random coil in a batch and inspected by an expert. Typically, in manual inspection, the inspected surface is about 0.05% of the total steel surface produced. In cold rolling mill complex, operators are sometimes stationed to inspect the finished product online for any defect. However, due to high line speed, fatigue and other adverse factors, inspection process is hardly satisfactory. Thus, the manual inspection process is not sufficient to guarantee defect-free surface of steel products withreasonable degree of confidence and naturally, need for automated surface inspection grew.In a significant development 1, nine steel companies and three aluminium companies in US started a research project in early 1980s on vision-based steel surface inspec-tion in collaboration with two commercial organisations. A prototype system was built and tested in several steel plants during 1987. At the same time, European companies also started working. Thus, from later half of 1980s, system-atic research work on surface inspection of steel products started. Today, vision-based automated surface inspection systems (ASIS) are produced by many reputed companies. Since 2006, an annual International Surface Inspection Summit (ISIS) is organised by a consortium of manufac-turers and others. Technology of vision-based automatic inspection of steel products, even though not 100% accurate has matured.This paper attempts to find out the status of development of vision-based ASIS for steel surfaces through review of published literature during the last two and a half decades.2. Complexities of steel surface inspection automationReal-time inspection of steel surfaces faces a number of challenges. The difficulties may be enumerated as follows:Hazardous site. The place for installation of inspec-tion equipment (illumination system, camera and some signal processing equipment), particularly, for hot roll-ing mills is very hazardous. Presence of high ambient temperature, dust, oil, water droplet and vapour is very common. Additionally, the illumination system and the cameras require protection against shock and vibration. Further, heavy equipment is moved in and out of site during daily, weekly and annual maintenance. All above factors necessitate the use of appropriate physical and environmental protective measures for site equipment.Operating speed. During regular production, operating speed of the surface to be inspected is generally high. For flat steel products, speed at the end of rolling, where the inspection equipment has to operate, is typically 20 m/s. For long products, particularly wire rods, speedcould be as high as 225 miles/h (100 m/s) 2. Real-time operation at such high speed requires special image pro-cessing equipment and software with small execution time.Varieties of surface defects in different steel products are reported to be very high 3. For example, Verlag Stahleisen 4 have categorised surface defects of hot-rolled products in nine main classes and 29 subclasses. These defects are not governed by any standard. Thus, their characteristics and classification vary from mill to mill and from operator to operator. Further, manifestation of these defects changes due to variations in production process.Large number of cameras. For flat steel products, two sets of inspection systems - one for top and another for bottom surface - are needed. Each of these sets in turn gen-erally consists of 3 to 4 cameras to cover the entire width of the strip. For long steel products, multiple cameras are to be located peripherally to ensure coverage of entire surface. For example, for a round product, at least three cameras are used while use of five cameras has been reported in the literature 5. Thus, gathering of images and their real-time processing is a daunting task.3. Prior literature reviewOver the years, a number of review papers 6-12 on vari-ous aspects of surface defect detection have been reported. Various aspects and methods for texture analysis have been reviewed in 13,14. Two comparatively recent review pa-pers are 6,7. Advances in surface defect detection using texture analysis techniques have been dealt with by Xie 6 covering applications in mainly textiles, tiles and wood. Kumar 7 has covered very comprehensively research work done in fabric surface defect detection and provided some valuable conclusions. Review papers particularly on texture defects and defects in fabrics also mention steel surface as a category where identified techniques can be applied. It is worth mentioning that as early as in 1982, 11 papers were listed under Inspection in Metal Processing Industry cat-egory in a review by Chin and Harlow 12. Gonzalez and Woods 15 provide an excellent theoretical background to all aspects of image processing, whereas theoretical basis for neural network-based classification is adequately cov-ered by Haykins 16. However, the authors could not locate any review of research work done in the field of steel surface defect detection and classification. Therefore, in this paper, attempt has been made to consolidate the published literature from academia, steel industry and manufacturers on the topic of automatic defect detection and classification of steel surfaces.4. Availability of research publications on automated vision-based steel surface inspectionAvailability of the published literature on steel surface in-spection mostly consists of research work done at various academic institutions, steel plants/steel plant researchunits and surface inspection equipment manufacturers. A number of research works have been published jointly by academic/research institutes and steel plants indicating good collaborative partnership. During the last 10 years, a significant percentage of published work on steel surface in-spection systems came from China. This is commensurate with Chinas dominant presence in steel manufacturing.Some papers have been published with reported research work mainly on defect classification aspects implemented in commercially procured systems. While overall systems and their benefits are well documented by reputed manu-facturers, details of defect detection and classification tech-niques are not elaborated, probably due to issues regarding intellectual property rights.5. Categories of steel surfacesTypes of steel surfaces studied for defect detection/classi-fication are: slab, billet, plate, hot strip, cold strip, rod/bar. They cover a large proportion of applications of steel as a material. cold strips, and off late, rod/bars have received more attention of researchers. This is mainly explained by the fact that large proportions of these products are finished product and quality requirements of customers have become more stringent over time.Broadly, steel surfaces can be categorised in flat and long products (Figure 2).Flat product surfaces can further be classified as follows: Slab/billet: both are produced by continuous casting process from liquid steel and have some similarity with respect to surface and internal conditions. Surface is scale covered and more grainy. Plates are produced by reheating a slab at about 1,250C and rolled subsequently. The surface is oxidised and comparatively even with respect to that of slab. Hot strips are produced by reheating a slab at about 1,250C and rolling in multiple rolling stands toreduce the thickness to desired value. The strip surface is oxidised. However, due to high rolling force, the surface granularity of hot strip is considerably reduced compared to slab. Cold strips are produced by rolling hot strips in cold rolling mill after pickling process (which removes the oxide layer and cleans the surface). Thus, the surface of cold strips is not oxidised, and the surface is quite smooth due to very high rolling forces used in cold deformation process. Coated strip (galvanised, tinned)/finished stainless strip surfaces are highly reflective in nature.Long product surfaces can further be classified as follows:Rods/bars are produced from billet by hot rolling process, and their surface is fairly oxidised. Further, the surface is also not flat, and therefore, angle of reflection varies towards the periphery thus producing nonuniform image intensity.Other long products like angles, channels, heavy struc-tural, rails etc. are produced from billet/bloom. They are of complex cross-section and require special lighting and camera arrangements.6. List of surface defects for steel productsThere is a large variety of surface defects for different steel products. Further, there is no agreed standard for defects. There is also large inter group similarity and intra group diversity 17 for various classes of defects, which makes defect classification difficult. Defect catalogues published by Verlag Stahleisen GmbH, Germany 4 act as defacto standards for this purpose.An attempt has been made to list some of the main de-fects which have been referred in the literature for surface defect detection and classification during the last two and a half decades. Defects have been listed vis-vis the categories of steel surfaces mentioned above.7. Key elements of automatic surface inspection system hardware structureFigure 3 shows the basic hardware structure of ASIS. It consists of one or more light source, one or more camera (bright field or both bright and dark field), fast image processor, server and the operator interface.7.1 Image acquisitionTo obtain satisfactory surface image quality, it is import-ant to illuminate the surface adequately and uniformly. In fact, high quality of illumination reduces computational burden of image processing. Two types of illumination techniques can be used for metallic surfaces: intensity im-aging and range imaging. 18-22 have discussed various aspects of illumination systems for metallic surfaces. Research on imaging systems for cold strips has been well documented in 23.Range imaging provides height information thereby making 3D defects prominent. Range imaging is not competitive to intensity imaging. In general, use of range imaging is not common in steel surface defect studies.Intensity imaging is primarily of two types: bright field and dark field. In bright field illumination, the sensor captures most of the directly reflected light. The surface appears bright, whereas the defect features appear darker. In dark field illumination, the angle of the incident light rays to the surface normal vector is very large. This results in a dark appearance of the surface, but some defects appear bright in the image. Dark field view requires more intense lighting. Requirement of about eight times compared to bright field lighting has been reported 21.Unfortunately, all surface defects do not show up either in bright field or in dark field alone. There are many examples of the use of two sets of cameras covering both the fields of view 24-26. Use of 20 charge-coupled device (CCD) area scan cameras which are used to capture surface image of both sides of hot-rolled strips using both bright field and dark field modes have been reported in aniron and steel plant of China 24. However, considering maintenance issues and system complexity, most of the systems place the cameras in between the bright field and dark field locations.7.2 Source of lightThe light source is required to provide uniform ripple-free light as far as possible. While ripple-free illumination calls for special arrangement of light power supply 27, provid-ing uniform intensity is not possible due to the use of more than one light source in majority of the cases. Figure 4 shows the variation of incident light intensity on to a steel surface using two xeon lights 28. Types of light source which are used in general are: wide spectrum tung-sten, fluorescent tubes, halogen, xeon and LED.7.3 Type of cameraIn general, high-resolution CCD cameras are used. Use of both line scan and area scan cameras has been reported in the literature. Line scan cameras have been widely used as it is easier to realise a strong and even illumination to the surface area to be inspected. The disadvantage with the line scan cameras is that they do not generate a complete image at once and requires an external hardware to build up images from multiple line scans 7. Most of the automatic surface inspection system manufacturers use line scan cam-era. For area scan cameras, the usage of transport encoder is optional and the inspection resolution in both directions is independent of the object (web) speed. However, while using area scan camera, special attention is needed to ensure even illumination of the total area under scan to the extent possible. High-resolution video cameras are also used as complimentary systems 30.7.4 Camera and image resolutionCamera resolution. Line scan camera resolution is generally 1,024(cross web) 1(down web) and 2,048 1 pixels. Yazdchi et al. 31 reported the use of 4,096 1 pixel camera. Manufacturers normally use 1,024/2,048/4,096 1 pixels. For area scan: 600 400 pixels have been reported by 32. In 33, 4,096 1,000 pixels have been used for slab.Image resolution. Various dimensions of image resolu-tions have been reported 24,26,31,33,34. Cross web reso-lutions vary from 0.17 mm to about 1 mm while reported down-web resolutions vary from 0.25 to 1.25 mm.7.5 Image processing computer hardwareImages captured by a CCD camera are transferred to some form of fast, parallel processing system dedicated to the camera and located close to it 24. The parallel processing system ensures real-time operation by processing bulk image data and selecting and storing regions of interest (RoIs). The parallel processing system could be a part of the camera itself, or a FPGA processor or a generalpurpose processor with special hardware. This part of the system is vitally important both from real-time operation as well as accuracy of defect detection and classification. Thereafter, a server with a large backup memory is used for further processing and for operators interface.8. List of defect detection and classification methodsVarious methods/techniques used for defect detection and classification of steel surfaces are listed in the litera-ture. Table 1 shows the list of different methods of de-fect detection vis-vis references obtained for this study. Types of steel surfaces have also been mentioned in the table. Techniques followed may broadly be cate-gorised as statistical, morphological, spatial domain filtering, frequency domain analysis, joint spatial/spatial-frequency analysis and fractal models. Spatial domain filtering, morphological operations and joint spatial/fre-quency domain filtering are found to be used extensively for all types of surfaces.Ultimate objective of surface inspection is to categorise defects in specified classes using classification tech-niques. As a process, classification starts after defects are localised by segmentation. At this stage, generally a number of features are extracted from regions of inter-est. Ideally, different combinations of these features are required to match uniquely with that of different types of defects. The matching is normally done using adap-tive learning methods such as neural network with back propagation (NN-BP), support vector machine (SVM)etc. Adaptive learning is of two types: a) supervised where the network is provided with a large number of known in-puts. Thereafter, the network produces the known outputs as closely as possible based on training. b) In unsupervised learning, the network is required to work out relationships between various inputs without being told.However, steel surface defects exhibit large inter group similarity and intra group diversity . Thus, finding suitable features and identifying classifiers with low computational cost are the major areas of research activ-ity. Table 2 shows the list of classification methods with respect to references and types of surface.ConclusionsThis paper dealt with review of automated inspectionmethods for steel surfaces using image processing techniques.Review of publications over two and a half decadeshas provided an idea of recent advances that have takenplace in this field.Main observations are as follows:a) Due to harsh environment of a steel mill, specialattention is required for design of illumination andimaging systems.Steel surface images are reportedto contain large amount of noise due to surfacescale, vibration, improper/variable illuminatio
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