Confusion matrix of SCF-YOLO detection results.

<div><p>Addressing the issues of large model size and slow detection speed in real-time defect detection in complex scenarios of printed circuit boards (PCBs), this study proposes a new lightweight defect detection model called SCF-YOLO. The aim of SCF-YOLO is to solve the problem of res...

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Bibliographic Details
Main Author:	Yazhou Li (277980) (author)
Other Authors:	Yuanyuan Wang (20181) (author), Jiange Liu (21011660) (author), Kexiao Wu (21011663) (author), Hauwa Suieiman Abdullahi (21011666) (author), Pinrong Lv (21011669) (author), Haiyan Zhang (176919) (author)
Published:	2025
Subjects:	Medicine Sociology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified xlink "> addressing printed circuit boards feature extraction network experimental results demonstrate combined loss function model &# 8217 large model size time defect detection novel scf module level semantic features slow detection speed yolo </ p defect detection detection speed model introduces inference speed yolov8 algorithm yolo utilizes yolo reduces training process thus improving synthesis c2f study proposes resource limitation precise location multiple scales model parameters lightweight mobilenet key features efficient solution effectively reduces effectively balance different objectives different levels complex scenarios combines ciou capture high algorithm deployment 60 %.
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Confusion matrix of SCF-YOLO detection results.

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