微型元器件引腳測距的深度學(xué)習(xí)方法研究

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中圖分類號(hào)：TP391.41 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào):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

微型電子元器件是電子系統(tǒng)中的關(guān)鍵組件，其質(zhì)量影響整個(gè)系統(tǒng)的性能。（剩余9824字）