基于特征提取和SVM分类的LED芯片缺陷快速检测与实现
LED Chip Defect Sorting System Based on Feature Extraction and SVM
吴乾生 1高健 1张揽宇 1郑卓鋆1
作者信息
- 1. 省部共建精密电子制造技术与装备国家重点实验室广东工业大学,广东 广州 510006
- 折叠
摘要
针对LED芯片工业化生产中人工目检缺陷检测效率低,速度慢,易疲劳,受主观影响等问题,这里研究设计了一套LED芯片缺陷的快速检测系统,提出了一种分角度多方向快速卷积、分区统计特征量、支持向量机分类的LED芯片缺陷识别算法,基于QT和Opencv开发了一套LED芯片缺陷快速检测与分类系统,搭建LED芯片检测系统的硬件平台,实现LED芯片实时在线缺陷识别和自动分拣.基于研发的LED芯片缺陷快速检测与分拣系统样机,对LED芯片进行了缺陷分拣的测试.
Abstract
Aiming at the problems of low efficiency,slow speed,fatigue,and subjective influence of manual visual inspection of defects in the industrial production of LED chips,this paper studies and designs a set of rapid detection system for LED chip de-fects,and propose an LED chip defect recognition algorithm based on multi-angle and multi-direction fast convolution,regional statistical feature quantity,and support vector machine classification.Based on QT and Opencv,a rapid detection and classifica-tion system for LED chip defects was developed,and the hardware platform for the LED chip detection system was built to realize real-time online defect recognition and automatic sorting of LED chips.Based on the developed LED chip defect sorting system prototype,the LEDchip was tested for defect sorting.
关键词
LED/特征量提取/PCA/支持向量机/多分类Key words
LED/Sobel/Feature Extraction/PCA/Support Vector Machine/Multi-Category引用本文复制引用
基金项目
国家自然科学基金面上项目(52075106)
出版年
2024
NETL
NSTL
万方数据