基于机器视觉的激光条形码识别技术研究
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摘要
采用当前条形码识别方法识别激光条形码时,所用的识别时间较长,得到的识别结果误差较大,存在识别效率低和识别结果准确率低的问题。提出基于机器视觉的激光条形码识别技术,将具有通信单元、光源、监视器、镜头、图像处理软件和图像处理单元的机器视觉系统应用于激光条形码的识别中,通过曝光值、帧率和分辨率选择机器视觉系统中的相机,采用最大似然法对激光条形码图像中的各帧做非线性拟合处理,并将激光条形码图像中位置相同的区域标记出来,对标记出来的区域做畸变校正处理,获得超分辨率的激光条形码图像。采用相似边距离方法计算激光条形码中字符的欧几里得距离,根据计算结果获得字符对应的数字,完成激光条形码的识别。实验结果表明,所提方法的识别效率高、识别结果准确率高。
When the current bar code recognition method is used to identify the laser bar code,the recognition time used is long,the obtained recognition result has large error,and there is a problem that the recognition efficiency and accuracy are low. A machine vision-based laser bar code recognition technology is proposed,which applies a machine vision system with communication unit,light source,monitor,lens,image processing software and image processing unit. Through exposure value,frame rate and resolution,select the camera in the machine vision system,use the maximum likelihood method to non-linearly fit each frame in the laser bar code image,mark the same area in the laser bar code image,and perform distortion correction on the marked area to get super-resolution laser bar code images. The Euclidean distance of the characters in the laser bar code is calculated by the similar edge distance method,and the number corresponding to the character is obtained according to the calculation result,and the recognition of the laser bar code is completed. The experimental results show that the proposed method has high recognition efficiency and accuracy of recognition results.
When the current bar code recognition method is used to identify the laser bar code,the recognition time used is long,the obtained recognition result has large error,and there is a problem that the recognition efficiency and accuracy are low. A machine vision-based laser bar code recognition technology is proposed,which applies a machine vision system with communication unit,light source,monitor,lens,image processing software and image processing unit. Through exposure value,frame rate and resolution,select the camera in the machine vision system,use the maximum likelihood method to non-linearly fit each frame in the laser bar code image,mark the same area in the laser bar code image,and perform distortion correction on the marked area to get super-resolution laser bar code images. The Euclidean distance of the characters in the laser bar code is calculated by the similar edge distance method,and the number corresponding to the character is obtained according to the calculation result,and the recognition of the laser bar code is completed. The experimental results show that the proposed method has high recognition efficiency and accuracy of recognition results.
引文
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[2]孔栋,孙亮,王建强,等.一种用于激光雷达识别车道标线算法[J].科学技术与工程,2017,17(16):87-92.
[3]张燕,许京,陈玲玲,等.基于激光距离传感器的路况识别系统的设计[J].激光与红外,2016,46(3):265-270.
[4]荆海峰,王力,王瑞鹏,等.三维激光扫描仪变形监测识别度测试方法[J].测绘科学,2017,42(10):176-180.
[5]张宾,宿敬肖,张微微,等.基于激光视觉的智能识别苹果采摘机器人设计[J].农机化研究,2016,38(7):60-64.
[6]唐庆菊,刘俊岩,王扬,等.基于模糊C均值聚类和Canny算子的红外图像边缘识别与缺陷定量检测[J].红外与激光工程,2016,001(9):274-278.
[7]路志英,刘书辰,宫志宏.基于多特征融合的玉米前期图像的旱情识别[J].激光与光电子学进展,2017,54(8):135-143.
[8]秦煜,吴静静,安伟.基于RANSAC的激光网格标记图像特征提取[J].计算机工程与科学,2017,39(8):1495-1501.
[9]徐苗,孙柯,王卓,等.激光散射图像检测桃果实货架期的食用价值[J].食品科学,2016,37(18):244-249.
[10]刘子腾,白瑞林,王秀平.基于激光视觉的角焊缝图像特征点提取[J].焊接学报,2016,37(2):89-93.
[11]滕继峣,秦凯,汪云甲,等.基于激光雷达观测的大气边界层自动识别局部最优点算法[J].光谱学与光谱分析,2017,37(2):361-367.
[12]赵宇枫,徐科.基于约束流体化的激光图像特征三维重建算法(英文)[J].机床与液压,2017,45(18):34-40.
[13]彭晨,余柏蒗,吴宾,等.基于移动激光扫描点云特征图像和SVM的建筑物立面半自动提取方法[J].地球信息科学学报,2016,18(7):878-885.
[14]王灿进,石宁宁,孙涛,等.基于光纹特征的激光主动照明图像去模糊[J].光学精密工程,2016,24(5):1159-1167.
[15]衡昊坤,蔡艳,盛洁,等.高功率光纤激光焊金属蒸气图像辐射亮度的三维重建[J].上海交通大学学报,2017,51(5):526-533.
[16]朱曼曼,时东锋,胡顺星,等.激光主动探测图像散斑自适应滤波抑制方法研究[J].量子电子学报,2017,34(2):145-153.
[17]马增强,王永胜,宋子彬.基于激光源的机车轮轨相对横移图像检测[J].图学学报,2017,38(4):623-628.