首页|基于线结构光的管棒材捆缝与捆丝识别方法研究

基于线结构光的管棒材捆缝与捆丝识别方法研究

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特钢管棒材精整过程需要对其进行打捆和拆捆操作，而人工拆捆工作环境差、强度高。针对拆捆过程捆缝状态和捆丝识别这一难题，提出一种基于线结构光的管棒材捆缝与捆丝识别方法。提出一种基于图像各亮度值像素数量的二值化阈值算法，通过计算满足亮度的像素点数量得到二值化阈值，以减弱环境光照影响;针对单张图片视野无法确定最大捆缝位置的问题，提出一种基于圆弧顶点和端点特征的激光图像拼接算法;最后，提出一种基于霍夫变换和连通域分析的捆丝数量和状态识别算法，实现对生产现场不同规格管棒材捆丝特征的精确识别。实验结果表明:提出的二值化阈值算法相较传统算法对激光条纹的识别率更高，且速度提升17％;最大捆缝和捆丝识别算法的检测误差小于3 mm，完全满足实际生产需要。
Research on Identification Method of Pipe Bar Binding Seam and Wire Based on Linear Structured Light
The finishing process of special steel tubes and bars requires bundling and unbundling operations,while manual unbund-ling has poor environment and high strength.A method for identifying the seam and wire of the bundle was proposed based on line struc-tured light.A binarization threshold algorithm was proposed based on the number of pixels of each luminance value of the image,and the binarization threshold was obtained by calculating the number of pixels that satisfied the luminance to reduce the influence of ambient light.For the problem that the maximum binding seam position could not be determined through the view field of a single image,a laser image mosaic algorithm based on arc vertex and endpoint features was proposed.Finally,a bundle number and state identification algo-rithm based on Hough transform and connected domain analysis was proposed to achieve accurate identification of the bundle character-istics of pipe and bar bundles with different sizes at the production site.The experimental results show that the proposed binarization threshold algorithm has a higher recognition rate to laser streaks compared with the traditional algorithms,and the speed is increased by 17％,the detection error of the maximum binding seam and wire identification algorithm is less than 3 mm.It fully meet the actual pro-duction needs.

tube and barunbinding robotlinear structured lightimage mosaicimage processing

马贺琛、马立东、马自勇

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太原科技大学机械工程学院,山西太原 030024

重载装备作业智能化技术与系统山西省重点实验室,山西太原 030024

太原重型机械装备协同创新中心,山西太原 030024

管棒材 拆捆机器人 线结构光 图像拼接 图像处理

国家自然科学基金面上项目山西省关键核心技术和共性技术专项山西省留学人员科技活动择优资助项目省筹资助留学回国人员项目山西省科技合作交流专项项目山西省应用基础研究计划

522743892020XXX009202200282022-160202104041101031201901D211292

2024

10.3969/j.issn.1001-3881.2024.05.004

机床与液压
中国机械工程学会 广州机械科学研究院有限公司

机床与液压

CSTPCD北大核心
影响因子：0.32
ISSN：1001-3881
年,卷(期)：2024.52(5)
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