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基于机器视觉的码坯异常检测与识别

Brick Stack Anomaly Detection and Recognition Based on Machine Vision

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摘要

针对砖瓦自动码坯中人工值守效率低、劳动强度大以及易漏检等问题,提出基于机器视觉的坯体异常自动检测与识别方法。分别采集分坯机和窖车上的坯体图像,采用改进的准十字中值滤波进行降噪处理;利用Canny算子提取坯体边缘;在分析坯体外形结构特点的基础上,采用极角约束的Hough变换对坯体纵向边缘直线段进行检测,提取每列坯体纵向完整度和横向宽度两个特征量对坯体进行异常识别。实验结果表明:在单层码坯和多层码坯方式下对掉坯、坯体错位和坯体倾斜的平均识别正确率为98.2%。能满足自动码坯系统中烧结普通砖坯体异常自动检测与识别的需求。

Abstract

Aiming at solving the problems such as low efficiency, high labor intensity and unsatisfying detection accuracy in traditional automatic brick stacking, a machine vision based automatic brick anomaly detection and recognition method is proposed. Brick images are captured from the brick delivering machine and the pit car are de-noised by applying an improved cross-like median filtering. Edges of bricks are extracted using the Canny edge detector. Vertical edges are detected by constraining polar angles in the Hough transform for analyzing the shape of the bricks. Anomaly detection is performed by measuring the length and width of the bricks in each column. Experimental results indicate that the average detection accuracy is 98.2% for brick-missing, brick-shifting and brick-tilting in one-scale brick stacks and multi-scale brick stacks. This meets the requirement of auto detection and recognition of brick anomaly in the automatic brick stack system of firing common bricks.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.4

DOI:10.3788/aos201131.0715002

所属栏目:机器视觉

基金项目:国家973计划(2007CB310600)资助课题。

收稿日期:2011-03-30

修改稿日期:2011-04-29

网络出版日期:--

作者单位    点击查看

向守兵:四川工程职业技术学院电气信息工程系, 四川 德阳 618000清华大学电子工程系, 北京 100084
苏光大:清华大学电子工程系, 北京 100084
陈健生:清华大学电子工程系, 北京 100084
刘京:清华大学电子工程系, 北京 100084
谭孝辉:四川工程职业技术学院电气信息工程系, 四川 德阳 618000

联系人作者:向守兵(8255@scetc.net)

备注:向守兵(1973—),男,硕士,副教授,主要从事模式识别与智能控制等方面的研究。

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引用该论文

Xiang Shoubing,Su Guangda,Chen Jiansheng,Liu Jing,Tan Xiaohui. Brick Stack Anomaly Detection and Recognition Based on Machine Vision[J]. Acta Optica Sinica, 2011, 31(7): 0715002

向守兵,苏光大,陈健生,刘京,谭孝辉. 基于机器视觉的码坯异常检测与识别[J]. 光学学报, 2011, 31(7): 0715002

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