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作 者:李现国[1,2] 郭宽宽 苗长云[1,2] LI Xianguo;GUO Kuankuan;MIAO Changyun(School of Electronics and Information Engineering,Tiangong University,Tianjin 300387,China;Tianjin Key Labratory of Optoelectronic Detection Technology and System,Tiangong University,Tianjin 300387,China)
机构地区:[1]天津工业大学电子与信息工程学院,天津300387 [2]天津工业大学天津市光电检测技术与系统重点实验室,天津300387
出 处:《天津工业大学学报》2025年第2期78-83,共6页Journal of Tiangong University
基 金:天津市重点研发计划科技支撑重点项目(18YFZCGX00930)。
摘 要:针对钢丝绳芯输送带接头抽动检测有效性和准确性差的问题,提出一种基于图像匹配的钢丝绳芯输送带接头抽动自动检测方法。首先进行图像预处理,提高图像质量;然后使用自动阈值提取钢丝绳端头点;再利用改进的ORB算法进行图像匹配,将待检测图像和基准图像中的钢丝绳端头点一一对应;最后采用基于钢丝绳端头基准点集的接头抽动测量方法计算接头抽动量并进行故障定位。实验结果表明:本文方法能够自动检测并标识出钢丝绳芯输送带接头处的钢丝绳端头点,准确得到钢丝绳端头点的匹配点对,计算出单个钢丝绳端头抽动量、钢丝绳上(下)端头平均抽动量和接头整体抽动量,检测并标识出异常钢丝绳端头的位置和变化量,实现了对接头抽动的自动检测和综合判断。Aiming at the problem of poor effectiveness and accuracy of joint twitch detection,an automatic detection method of steel wire rope core conveyor belt joint twitch based on image matching is proposed.Firstly,image preprocessing is carried out to improve the image quality.Then the automatic threshold is used to extract the wire rope end point.Then,the improved ORB algorithm is used for image matching,and the steel wire rope end points in the detected image and the reference image are corresponded one by one.Finally,the joint twitch measurement method based on the set of wire rope end reference points is used to calculate the joint twitch and locate the fault.The experimental results show that this method can automatically detect and identify the end points of the steel wire rope at the joint of the steel wire rope core conveyor belt,accurately obtain the matching point pairs of the end points of the steel wire rope,calculate the end twitch of a single steel wire rope,the average end twitch of the steel wire rope upper(lower)end and the overall end twitch,and detect and identify the position and variation of the abnormal end points of the steel wire rope.And the automatic detection and comprehensive judgment of joint twitch are realized.
关 键 词:钢丝绳芯输送带 接头抽动 图像匹配 钢丝绳端头点提取 ORB算法
分 类 号:TD77.1[矿业工程—矿井通风与安全] TP391[自动化与计算机技术—计算机应用技术]
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