基于支持向量机红外图像分割的输送带纵向撕裂检测方法  

Detection method of belt longitudinal tear based on support vector machine and infrared image segmentation

作　　者：赵弼龙[1] 乔铁柱[1] 

机构地区：[1]太原理工大学新型传感器与智能控制教育部重点实验室,山西太原030024

出　　处：《工矿自动化》2014年第5期30-33,共4页Journal Of Mine Automation

基　　金：教育部科技研究重点项目(210270)

摘　　要：针对现有输送带纵向撕裂检测方法存在检测精度低、无法消除煤矿井下复杂环境的影响问题,提出了一种基于支持向量机红外图像分割的输送带纵向撕裂检测方法。该方法首先采集输送带纵向撕裂红外图像,然后利用支持向量机对红外图像进行分割,最后通过计算撕裂像素点数目,准确检测出输送带纵向撕裂或预测纵向撕裂趋势。试验测试结果表明,采用该方法实现图像分割时间短,检测精度可达99.1%。In view of problems of low detection precision of existing detection methods of belt longitudinal tear and difficulty to eliminate influence of coal mine complex environment, a detection method of belt longitudinal tear based on support vector machine and infrared image segmentation was proposed. Firstly, the infrared image of belt longitudinal tear is collected . Then, the infrared image is segmented by the method of support vector machine. Finally, the belt longitudinal tear or its tendency is tested accurately by calculating quantity of torn pixels. The test results show that the image segmentation time of the method is short and detection precision is high to 99.1%.

关 键 词：输送带 纵向撕裂 红外图像分割 支持向量机 

分 类 号：TD634[矿业工程—矿山机电]