基于改进YOLOv4算法的烟丝宽度检测方法

METHOD FOR DETECTING TOBACCO FIBER WIDTH BASED ON IMPROVED YOLOv4 ALGORITHM

  • 摘要: 烟丝的宽度是衡量卷烟质量的重要指标,为了能够实时把控烟丝质量,该文设计一种烟丝宽度实时在线检测方法。该文将YOLOv4中的SPP和PANet结构进行融合,并将输出三种输出尺度改为一种输出尺度,使改进后的YOLOv4算法结构更加简单,在性能几乎不下降的情况下,运算速度提升近35%;烟丝宽度计算方法是该文设计的MCS(Moving Center Search)中心移动搜索法。该方法能够自动搜索出烟丝的两个侧边,并较为精确地计算出烟丝宽度,计算精度可达0.2毫米。将MCS烟丝宽度计算方法与该文改进后的YOLOv4模型相结合,能够进行实时在线的烟丝宽度检测,实时把控烟丝生产质量,提升生产效率。

     

    Abstract: The width of the tobacco fiber is an important indicator of the quality of the cigarette. In order to be able to control the quality of tobacco fiber width in real-time, this paper designs a real-time online detection method of tobacco fiber width. The SPP and PANet structure were fused in YOLOv4, and the three output scales were changed into one output scale making the improved YOLOv4 algorithm simpler, and the calculation speed increased nearly 35% in the same case of performance. The width calculation method was the MCS (Moving Center Search) center mobile search method, which could automatically search for both sides of the tobacco fiber, and more accurately calculate the width with the calculation accuracy of 0.2 mm. The MCS tobacco fiber width calculation method was combined with the improved YOLOv4 model of this paper, and real-time online tobacco fiber width detection was performed to control the quality of tobacco fiber in real-time and improve production efficiency.

     

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