基于改进KNN边缘滤波算法的陶瓷绝缘子表面检测研究

Research on surface detection of ceramic insulator based on image processing

高压线上陶瓷绝缘子全表面检测是保证其质量的重要一环,由于其表面复杂,目前主流是人工检测,漏检、错检无法避免。用带有机器视觉的自动化装置来检测陶瓷绝缘子是近年来的趋势,本文针对绝缘子的气泡、裂纹等主要缺陷进行识别,提出最优滤波器方法加大气泡缺陷对比度,对气泡ROI进行定位和提取,通过改进KNN边缘滤波来预处理图片,对裂纹进行定位识别,最后通过特征进行筛选。该方法能够快速、准确地识别陶瓷表面缺陷特征,识别效率在200 ms以内,识别率达到了98.2%,满足高压线陶瓷行业的精度需求。

The full surface detection of ceramic insulators on high-voltage lines is an important part of ensuring its quality.Due to the complexity of its surface,manual detection is the mainstream at present,and missed detection and false detection are inevitable.It is a trend in recent years to use automatic devices with machine vision to detect ceramic insulators.This paper identifies the main defects of insulators,bubbles and cracks,and preprocesses the pictures by improving KNN edge filtering.The weighted fitting method extracts bubble defects.Bubble ROI is used for positioning and extraction,threshold segmentation method,morphological crack processing and crack positioning,and finally through feature screening,this method can quickly and accurately identify the characteristics of ceramic surface defects,the recognition efficiency is within 200 ms,and the recognition rate has reached 98.2%,meeting the precision requirements of the high-voltage line ceramic industry.