The knitting needle cylinder is one of the core parts of a hosiery machine. The operation of its needles can directly affect the production quality and efficiency of the hosiery machine. To reduce the production loss of a hosiery machine caused by knitting needle faults, a knitting needle fault detection system for hosiery machines based on a synergistic combination of laser detection and machine vision is proposed in this paper. When the system was operating normally, a photoelectric detector collected the laser signal reflected by the knitting needle and the system monitored the operation of the knitting needle using the ratio of adjacent peak-to-peak distances of the signals. When a fault signal was detected, the hosiery machine was stopped by the system immediately, and a charge-coupled device camera was used to take an image of the faulty knitting needle. After image preprocessing, the faulty knitting needle could be identified quickly and accurately using an image region size classifier based on a decision tree. The experimental results showed that a single image classification by the classifier could be performed in as little as 0.002 s.

中文翻译：

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基于激光检测和机器视觉的袜机织针故障检测系统

织针筒是织袜机的核心部件之一。其织针的运行状况直接影响织袜机的生产质量和效率。为减少织针故障造成的针织机生产损失，提出了一种基于激光检测与机器视觉协同组合的针织机织针故障检测系统。系统正常运行时，光电探测器采集织针反射的激光信号，系统利用信号的相邻峰峰值比来监测织针的运行情况。当检测到故障信号时，系统立即停止织袜机，并使用电荷耦合器件相机拍摄故障织针的图像。图像预处理后，使用基于决策树的图像区域大小分类器可以快速准确地识别出故障织针。实验结果表明，分类器对单个图像的分类可以在短短 0.002 秒内完成。