Improved printed circuit board defect detection scheme

Abstract In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effec...

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Main Authors: Lufeng Bai, Wen Hao Xu
Format: Article
Language:English
Published: Nature Portfolio 2025-01-01
Series:Scientific Reports
Subjects:
Online Access:https://doi.org/10.1038/s41598-025-85245-2
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author Lufeng Bai
Wen Hao Xu
author_facet Lufeng Bai
Wen Hao Xu
author_sort Lufeng Bai
collection DOAJ
description Abstract In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effectively enhances the detection performance of PCB small defects through multiple innovative designs. First, we incorporate the Efficient Channel Attention Network (ECANet) attention mechanism into the backbone network of YOLOv8, which improves the performance of small-target detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. Second, we optimize and upgraded the neck structure. On the one hand, the $$C2f_E$$ module is introduced to facilitate cross-layer feature fusion to ensure that the rich texture information at the lower layer and the abstract semantic information at the higher layer complement each other, which is conducive to improving the contextual understanding of small target detection; on the other hand, a detection head specialized for small targets is designed and added to enhance the ability of locating and identifying tiny defects. Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. Compared to the original YOLOv8 algorithm, this algorithm improves the average accuracy on small targets by $$5.5\%$$ for mAP50.
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spelling doaj-art-779eff324b9e484d987af3be89dafaf52025-01-19T12:18:44ZengNature PortfolioScientific Reports2045-23222025-01-0115111310.1038/s41598-025-85245-2Improved printed circuit board defect detection schemeLufeng Bai0Wen Hao Xu1School of Computer Engineering , Jiangsu Second Normal UniversitySchool of Computer Engineering , Jiangsu Second Normal UniversityAbstract In this paper, an improved printed circuit board(PCB)defect detection scheme named PD-YOLOv8 is proposed, which is specialized in the common and challenging problem of small target recognition in PCB inspection. This improved scheme mainly relies on the basic framework of YOLOv8n, and effectively enhances the detection performance of PCB small defects through multiple innovative designs. First, we incorporate the Efficient Channel Attention Network (ECANet) attention mechanism into the backbone network of YOLOv8, which improves the performance of small-target detection by adaptively enhancing the expressiveness of key features, so that the network possesses higher sensitivity and focus on tiny details in PCB images. Second, we optimize and upgraded the neck structure. On the one hand, the $$C2f_E$$ module is introduced to facilitate cross-layer feature fusion to ensure that the rich texture information at the lower layer and the abstract semantic information at the higher layer complement each other, which is conducive to improving the contextual understanding of small target detection; on the other hand, a detection head specialized for small targets is designed and added to enhance the ability of locating and identifying tiny defects. Furthermore, in order to further enhance the interaction and fusion of multi-scale features, we also add a SlimNeck module to the neck structure, which realizes efficient information transfer through streamlined design and reduces computational complexity at the same time. In addition, we draw on the advanced BiFPN structure, which enables the bidirectional flow of feature information between multiple layers and greatly improves the capture and integration of small target features. Compared to the original YOLOv8 algorithm, this algorithm improves the average accuracy on small targets by $$5.5\%$$ for mAP50.https://doi.org/10.1038/s41598-025-85245-2YOLOv8nPCB defect detectionSmall targetAttention mechanism
spellingShingle Lufeng Bai
Wen Hao Xu
Improved printed circuit board defect detection scheme
Scientific Reports
YOLOv8n
PCB defect detection
Small target
Attention mechanism
title Improved printed circuit board defect detection scheme
title_full Improved printed circuit board defect detection scheme
title_fullStr Improved printed circuit board defect detection scheme
title_full_unstemmed Improved printed circuit board defect detection scheme
title_short Improved printed circuit board defect detection scheme
title_sort improved printed circuit board defect detection scheme
topic YOLOv8n
PCB defect detection
Small target
Attention mechanism
url https://doi.org/10.1038/s41598-025-85245-2
work_keys_str_mv AT lufengbai improvedprintedcircuitboarddefectdetectionscheme
AT wenhaoxu improvedprintedcircuitboarddefectdetectionscheme