{"title":"MuSAP-GAN:利用基于生成对抗网络的多层次注意力印刷电路板缺陷检测","authors":"Nileshkumar Patel","doi":"10.1007/s00202-024-02703-2","DOIUrl":null,"url":null,"abstract":"<p>A printed circuit board (PCB) is one of the important components in every single electronic device, which assists in connecting each component for many purposes. Somehow, the PCB can be affected due to spurs, short circuits, mouse bites, and so on. Therefore, the detection strategy for such defects is very important and also complicated. So, this research concentrates on developing a deep learning model, a multi-level attention-based printed circuit board with a generative adversarial network, and a YOLOv5 (MuAP-GAN-YOLOv5) model for defect detection in PCB. The contribution of this research is to enhance image quality using the proposed multi-level attention-based PCB-GAN (MuAP-GAN) method, which is embedded with a multi-level attention mechanism to enhance image quality. Therefore, the model can efficiently learn and train for accurate defection as well as localize the defected area in PCB. Here, the YOLOv5 model plays an important role in training based on enhanced features and, therefore provides accurate results. In addition, this model requires less computational expenses, is quite reliable, also provides a maximum accuracy of 95.24% compared to other traditional methods.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MuSAP-GAN: printed circuit board defect detection using multi-level attention-based printed circuit board with generative adversarial network\",\"authors\":\"Nileshkumar Patel\",\"doi\":\"10.1007/s00202-024-02703-2\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>A printed circuit board (PCB) is one of the important components in every single electronic device, which assists in connecting each component for many purposes. Somehow, the PCB can be affected due to spurs, short circuits, mouse bites, and so on. Therefore, the detection strategy for such defects is very important and also complicated. So, this research concentrates on developing a deep learning model, a multi-level attention-based printed circuit board with a generative adversarial network, and a YOLOv5 (MuAP-GAN-YOLOv5) model for defect detection in PCB. The contribution of this research is to enhance image quality using the proposed multi-level attention-based PCB-GAN (MuAP-GAN) method, which is embedded with a multi-level attention mechanism to enhance image quality. Therefore, the model can efficiently learn and train for accurate defection as well as localize the defected area in PCB. Here, the YOLOv5 model plays an important role in training based on enhanced features and, therefore provides accurate results. In addition, this model requires less computational expenses, is quite reliable, also provides a maximum accuracy of 95.24% compared to other traditional methods.</p>\",\"PeriodicalId\":50546,\"journal\":{\"name\":\"Electrical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.6000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electrical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00202-024-02703-2\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02703-2","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
MuSAP-GAN: printed circuit board defect detection using multi-level attention-based printed circuit board with generative adversarial network
A printed circuit board (PCB) is one of the important components in every single electronic device, which assists in connecting each component for many purposes. Somehow, the PCB can be affected due to spurs, short circuits, mouse bites, and so on. Therefore, the detection strategy for such defects is very important and also complicated. So, this research concentrates on developing a deep learning model, a multi-level attention-based printed circuit board with a generative adversarial network, and a YOLOv5 (MuAP-GAN-YOLOv5) model for defect detection in PCB. The contribution of this research is to enhance image quality using the proposed multi-level attention-based PCB-GAN (MuAP-GAN) method, which is embedded with a multi-level attention mechanism to enhance image quality. Therefore, the model can efficiently learn and train for accurate defection as well as localize the defected area in PCB. Here, the YOLOv5 model plays an important role in training based on enhanced features and, therefore provides accurate results. In addition, this model requires less computational expenses, is quite reliable, also provides a maximum accuracy of 95.24% compared to other traditional methods.
期刊介绍:
The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed.
Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).