微型元器件引脚测距的深度学习方法研究

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中图分类号：TP391.41 文献标识码：A 文章编号:1672-0105(2025)02-0047-05

Research on Deep Learning Methods for Micro-component Pin Distance Measurement

XIANG Chaohui, QIAN Yuezhong, ZOU Jiaming (ZhejiangIndustry&TradeVocationalCollege,Wenzhou325o35,China）

Abstract:Inindustrial visualquality inspection,measuring the distancebetween pinsof microelectroniccomponents posesa significantchalenge.TraditionalmethodsrelyheavilyonOpenCVforextensive imageprocesingoperations,whichrequieegiees torepeatedlyadjustparameters,leading toloweiciencyandinsuficientauracyIntherealmofdeepleaing,theseisuscanbe addressedthrough distanceregresionandobject detectiontechniques.MethodOneivolvesusingtheResNet18 network to extract image features and employing fullyconnectedlayers fordistanceregresion toultimately measure pin distances.Method Two trains the YOLOv8n modelto detect pin bounding boxes and then calculates the distances between pins basedon these detections. Aditionallyduetoteghomogeneityofimagescolectedfromproductionlinswherepinpositionsarelargelysimlaodels oftensuffer fromsevereoverfiting.Tomitigatethis,wedesignedasimulated pindistancemeasurementmethodtogeneratediverse data,therebyenhancingtherobustnessofthemodels.Experimentalresultsdemonstratethatthedep learing-baseddistance measurement methods offer a significant improvement in acuracy compared to traditional OpenCV-based approaches.

Keywords: industrial quality inspection; deep learning; object detection; pin distance measurement

0引言

微型电子元器件是电子系统中的关键组件，其质量影响整个系统的性能。（剩余9824字）