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

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Electrical Engineering Pub Date : 2024-09-16 DOI:10.1007/s00202-024-02703-2
Nileshkumar Patel
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引用次数: 0

摘要

印刷电路板(PCB)是每个电子设备中的重要组件之一,它可以帮助连接每个组件以实现多种目的。印刷电路板可能会因刺伤、短路、鼠咬等原因受到影响。因此,这类缺陷的检测策略非常重要,也很复杂。因此,本研究集中开发了一个深度学习模型、一个基于生成式对抗网络的多级注意力印刷电路板,以及一个用于 PCB 缺陷检测的 YOLOv5(MuAP-GAN-YOLOv5)模型。本研究的贡献在于利用所提出的基于多级注意的印刷电路板生成式对抗网络(MuAP-GAN)方法提高图像质量,该方法嵌入了多级注意机制,以提高图像质量。因此,该模型可以高效地学习和训练准确的缺陷,并定位 PCB 中的缺陷区域。在此,YOLOv5 模型在基于增强特征的训练中发挥了重要作用,因此能提供准确的结果。此外,与其他传统方法相比,该模型所需的计算费用更低,相当可靠,还能提供 95.24% 的最高准确率。
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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.

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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
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).
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