摘要
输送带撕裂检测是煤矿安全生产中非常重要的部分。本文提出了一种新的输送带纵向撕裂检测方法—视听融合检测方法。视听融合方法使用可见光CCD和麦克风阵列采集输送带在不同运行状态下的图像和声音。通过对采集到的图像和声音进行处理和分析,分别提取出正常、撕裂和划伤的图像和声音特征。然后利用机器学习算法对提取的图像和声音特征进行融合和分类。实验结果表明,视听融合检测方法对输送带纵向撕裂的准确率为96.23%,对于输送带划伤的准确率为93.66%,与现有方法相比,该方法克服了传统机器视觉检测条件的局限性,对于输送带撕裂检测更加准确可靠。
Conveyor belt tear detection is a very important part of coal mine safety production.In this paper,a new method of detecting conveyor belt damage named audio-visual fusion(AVF)detection method is proposed.The method uses both a visible light CCD and a microphone array to collect images and sounds of the conveyor belt in different running states.By processing and analyzing the collected images and sounds,the image,and sound features of normal,tear and scratch can be extracted respectively.Then the extracted features of images and sounds are fused and classified by machine learning algorithm.The results show that the accuracy of AVF method for conveyor belt scratch is 93.66%,and the accuracy of longitudinal tear is higher than 96.23%.Compared with existing methods AVF method overcomes the limitation of visual detection condition,and is more accurate and reliable for conveyor belt tear detection.
作者
高瑜璋
乔铁柱
车剑
Gao Yuzhang;Qiao Tiezhu;Che Jian(Key Laboratory of Advanced Transducers and Intelligent Control System,Ministry of Education and Shanxi Province,Taiyuan University of Technology,Taiyuan 030024,China)
出处
《电子测量技术》
北大核心
2022年第7期131-136,共6页
Electronic Measurement Technology
基金
国家自然科学基金(NSFC-山西煤基低碳联合基金项目U1810121)
2020年中央引导地方科技发展资金项目(YDZX20201400001796)资助。
关键词
图像特征提取
声音特征提取
特征融合
纵向撕裂检测方法
机器学习
image feature extraction
audio feature extraction
feature fusion
longitudinal tear detection method
machine learning
