首页|基于特征提取和SVM分类的LED芯片缺陷快速检测与实现

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

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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.
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.

LEDSobelFeature ExtractionPCASupport Vector MachineMulti-Category

吴乾生、高健、张揽宇、郑卓鋆

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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

LED 特征量提取 PCA 支持向量机 多分类

国家自然科学基金面上项目

52075106

2024

机械设计与制造
辽宁省机械研究院

机械设计与制造

CSTPCD北大核心
影响因子:0.511
ISSN:1001-3997
年,卷(期):2024.400(6)