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基于机器视觉的镜片表面缺陷检测系统研发 被引量:2

Reaearch of Lens Surface Detect System Based on Matlab
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摘要 针对传统镜片企业人工抽样检测的弊端,进行机器视觉测量系统的搭建,以图像处理技术为基础,利用matlab进行缺陷识别算法的开发,判断是否有缺陷,将数据传送至arduino单片机,通过单片机的处理,通过机械手将缺陷镜片剔除,并在人机可视化界面上显示缺陷,从而实现机器视觉在线检测。该系统性能稳定、能够大幅提高检测效率和精确度,同时还能够减少大量劳动力,降低检测工作对工人眼睛的伤害,产生极大的经济效益和社会效益。 For dealing with the disadvantages of manual sampling inspection of traditional lens enterprises,this thesis is written to in- troduce a mechanical visual measurement system. Based on image processing technologtgy,this system is to tesi lens surface defect by using Matlab defeet recognition algorithm. Data gathered from the system will be conveyed to Arduino MCU. After processing,the defect lens will be selected out by mechanical arms. Meanwhile,the defects are to be shown on the visual interface of computer,and thus making the re- al-time detection feasible. The mechanical visual measurement system is of great stability,eficieney and accuracy. It also could decrease labor consumption and reduce the harm to workers' eyes. It is of high benefits to introduce the mechanical visual measurement system.
出处 《黑龙江科技信息》 2016年第22期22-23,共2页 Heilongjiang Science and Technology Information
基金 国家大学生创新创业训练项目(201510386003)
关键词 机器视觉 图像处理 单片机 人机可视化界面 image processing defect recognition computational theory of edge delection GUl
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  • 1胡亮,段发阶,丁克勤,叶声华.基于线阵CCD钢板表面缺陷在线检测系统的研究[J].计量学报,2005,26(3):200-203. 被引量:32
  • 2李东源,张晓光,闫秀生,侯蓝田,周桂耀,郑荣山.CCD摄像机大视场光学镜头的设计[J].应用光学,2006,27(2):105-107. 被引量:20
  • 3韩英莉,颜云辉.基于BP神经网络的带钢表面缺陷的识别与分类[J].仪器仪表学报,2006,27(12):1692-1694. 被引量:27
  • 4[2]Rautaruukki New Technology. Defect Classification in Surface Inspection of Strip Steel. Steel Times, 1992(5): 214~216
  • 5[3]Badger J C, Enright Sean T. Automated surface inspection system. Iron and Steel Engineer, 1996 (3): 48~51
  • 6[4]Parsytech Computer GmbH. Software controlled on-line surface inspection. Steel Times International, 1998(3): 30~35
  • 7[5]Karayiannis N B. Accelerating the training of feed forward Neural Networks using generalized hebbian rules for inintializing the internal representation. IEEE Transactions on Neural Networks, 1996, (7)2: 419~426
  • 8[6]Sking J, Jorg R. Self-learning fuzzy controllers based on temporal back propagation. IEEE Trans. on Neural Networks, 1992, 3(5): 714~723
  • 9[7]Amari S, Murata N, Muller K R, et al. Asymptotic statistical theory of overtraining and cross-validation. In: Anon. ed. METR 95-06. Tokyo: Dept. of Mathematical Engineering and Information, Physics, Univ. of Tokyo, 1995.
  • 10David G,Park.Practical application of on-1ine hot strip inspection system at Hoogovens[J].Iron and Steel Engineer,1995,72 (7):40-44.

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