基于卷积滤波的纱筒定位检测算法  

Yarn Bobbin Positioning Detection Algorithm Based on Convolution Filtering

作　　者：王宏鹏 王俊茹[1] 汝欣 史伟民[1] 

机构地区：[1]浙江理工大学机械工程学院,浙江 杭州

出　　处：《建模与仿真》2023年第3期2486-2497,共12页Modeling and Simulation

摘　　要：纱筒定位检测是自动换筒算法中的首要任务,针对纱筒图像背景复杂、分割困难的问题,本文提出一种基于卷积滤波的纱筒定位算法。首先对图像进行直方图均衡化,增强区域特征,再对传统扩散滤波模型进行优化,抑制纹理等高频信息,接着设计圆形卷积核对图像进行卷积滤波,抑制图像背景,最后对卷积图像进行Blob分析,通过区域特征筛选得到内筒轮廓。在自主搭建的检测平台上使用本文方法进行实验,实验结果证明纱线余量的检测精度在1.5%以内,满足实际生产要求,为纺织产业自动化生产提供一定的依据。Yarn bobbin positioning detection is the primary task in the automatic replacement algorithm. Aiming at the problem of complex background and difficult segmentation of yarn bobbin image, this paper proposes a yarn bobbin positioning algorithm based on convolution filtering. Firstly, histo-gram equalization is performed on the image to enhance regional features. Then the traditional diffusion filtering model is optimized to suppress high-frequency information such as texture. Then a circular convolution kernel is designed to filter the image and suppress the image background. Finally, Blob analysis is performed on the convolution image, and the inner bobbin contour is ob-tained by regional feature screening. Experiments are carried out using this method on the self-built detection platform. The experimental results show that the detection accuracy of yarn margin is within 1.5%, which meets the actual production requirements. It provides a certain basis for the automated production of the textile industry.

关 键 词：直方图均衡化 卷积滤波 BLOB分析 图像背景 特征筛选 检测平台 扩散滤波 定位检测 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]