COAL TRANSPORTATION AREA DETECTION ALGORITHM OF BELT CONVEYOR BASED ON SEMANTIC SEGMENTATION
赵学军 1杨征 1闫雪1
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作者信息
1. 中国矿业大学(北京)机电与信息工程学院 北京 100083
折叠
摘要
针对煤炭运输过程中,经常无法保持煤炭在带式输送机上的运量均匀,使得带式输送机长时间全速运转而造成电能浪费和设备无效磨损的问题,提出一种基于语义分割的带式输送机煤料运输区域检测算法.该算法在DeeplabV3+的基础上,根据特征通道之间的相互依赖关系,引入注意力机制,使用不同扩张率的卷积核获得多种尺度的语义信息,来精确分割出煤炭在带式输送机的运输区域.实验结果表明,该算法平均交并比(Mean Intersection over Union,MIoU)相比于DeeplabV3+算法提高 1.24 百分点,能够有效精准地分割出煤料的运输区域,为煤量估计工作提供有效的保障.
Abstract
In the coal transportation process,it is often impossible to maintain the uniform transportation volume of coal on the belt conveyor,which causes the belt conveyor to run at full speed for a long time,resulting in waste of electricity and ineffective equipment wear.This paper proposes a semantic segmentation-based belt conveyor coal transportation area detection algorithm.Based on DeeplabV3+,the algorithm introduced an attention mechanism according to the interdependence between characteristic channels,and used convolution kernels with different expansion rates to obtain semantic information of multiple scales and precisely segment the coal transportation area on the belt conveyor.The experimental results show that the mean intersection over union(mIoU)of the algorithm is 1.24 percentage points higher than that of the DeeplabV3+algorithm,which can effectively and accurately segment the coal transportation area,and provide an effective guarantee for the estimation of coal volume.
关键词
带式输送机/煤量检测/煤料运输区域/语义分割/注意力机制
Key words
Belt conveyor/Coal quantity detection/Coal transportation area/Semantic segmentation/Attention mechanism
维普
万方数据