An Enhanced Detection Method of PCB Defect Based on Improved YOLOv7

Yang, Yujie; Kang, Haiyan

doi:10.3390/electronics12092120

Cited by 19 publications

(7 citation statements)

References 31 publications

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“…Yang et al. [30] utilise shuffle attention to improve YOLOv7 for detecting surface defects on printed circuit boards. Coupled head scheme is utilised in YOLOv7.…”

Section: Related Workmentioning

confidence: 99%

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Chen,

Deng,

Gao

et al. 2024

IET Image Processing

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Surface defect detection is an essential task for ensuring the quality of products. Many excellent object detectors have been employed to detect surface defects in resent years, which has achieved outstanding success. To further improve the detection performance, a defect detector based on state‐of‐the‐art YOLOv8, named improved YOLOv8 by neck, head and data (NHD‐YOLO), is proposed. Specifically, YOLOv8 from three crucial aspects including neck, head and data is improved. First, a shortcut feature pyramid network is designed to effectively fuse features from backbone by improving the information transmission. Then, an adaptive decoupled head is proposed to alleviate the feature spatial misalignment between the classification and regression tasks. Finally, to enhance the training on small objects, a data augmentation method named selective small object copy and paste is proposed. Extensive experiments are conducted on three real‐world datasets: detection dataset from Northeastern University (NEU‐DET), printed circuit boards from Peking University (PKU‐Market‐PCB) and common objects in context (COCO). According to the results, NHD‐YOLO achieves the highest detection accuracy and exhibits outstanding inference speed and generalisation performance.

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“…Yang et al. [30] utilise shuffle attention to improve YOLOv7 for detecting surface defects on printed circuit boards. Coupled head scheme is utilised in YOLOv7.…”

Section: Related Workmentioning

confidence: 99%

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Chen,

Deng,

Gao

et al. 2024

IET Image Processing

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“…Defects that could be recognized are: missing hole, mouse bite, open, short, spur, and spurious copper. Yang and Kang [39] present a method for PCBs defect detection, which is based on the improved YOLOv7 network. The method uses the SwinV2_TDD module for PCB feature extraction and magnification factor shuffle attention mechanism for improving the attention mechanism adaptability.…”

Section: Research Related To Defects Detection On Pcb and Pcbamentioning

confidence: 99%

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

Petkov,

Ivanova

2024

Bulletin EEI

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Testing and visual inspection of printed circuit boards (PCBs) and printed circuit board assemblies (PCBAs) are important procedures in the manufacturing process of electronic modules and devices related to locating and identifying possible defects and failures. Earlier defects detection leads to decreasing expenses, time and used resources to produce high quality electronics. In this paper an exploration and analysis about the current research regarding methods for PCB and PCBA testing, techniques for defects detection and vusial inspection is performed. The impact of machine and deep learning for testing and visual inspection procedures is also investigated. The used methodology comprises bibliometric approach and content analysis of papers, indexed in scientific database Scopus, considering the queries: “PCB and testing” and “PCB and testing”, “printed circuit board assembly and testing” and “PCBA and testing”, “PCB defect detection” and “PCBA defect detection”, “PCB and visual inspection”, and “PCBA and visual inspection”. The findings are presented in the form of a framework, which summarizes the contemporary landscape of methods for PCBs and PCBAs testing and visual inspection.

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“…Additionally, both convolutional and attentional mechanisms were integrated to enhance the network's feature extraction capacity. Yang et al [97] introduced an enhanced YOLOv7 model. They achieved this enhancement by formulating the SwinV2_TDD module, which facilitates the extraction of local PCB information through the incorporation of an added convolutional layer.…”

Section: Based Transformer Algorithmmentioning

confidence: 99%

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

Chen,

Wu,

et al. 2023

IEEE Access

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A printed circuit board (PCB) functions as a substrate essential for interconnecting and securing electronic components. Its widespread integration is evident in modern electronic devices, spanning computers, cell phones, televisions, digital cameras, and diverse apparatus. Ensuring product quality mandates meticulous defect inspection, a task exacerbated by the heightened precision of contemporary circuit boards, intensifying the challenge of defect detection. Conventional algorithms, hampered by inefficiency and limited accuracy, fall short of usage benchmarks. In contrast, PCB defect detection algorithms rooted in deep learning hold promise for achieving heightened accuracy and efficiency, bolstered by their adeptness at discerning novel defect types. This review presents a comprehensive analysis of machine vision-based PCB defect detection algorithms, traversing the realms of machine learning and deep learning. It commences by contextualizing and elucidating the significance of such algorithms, followed by an extensive exploration of their evolution within the machine vision framework, encompassing classification, comparison, and analysis of algorithmic principles, strengths, and weaknesses. Moreover, the introduction of widely used PCB defect detection datasets and assessment indices enhances the evaluation of algorithmic performance. Currently, the detection accuracy can exceed 95% at an Intersection over Union (IoU) of 0.5. Lastly, potential future research directions are identified to address the existing issues in the current algorithm. These directions include utilizing Transformers as a foundational framework for creating new algorithms and employing techniques like Generative Adversarial Networks (GANs) and reinforcement learning to enhance PCB defect detection performance.

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An Enhanced Detection Method of PCB Defect Based on Improved YOLOv7

Cited by 19 publications

References 31 publications

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

NHD‐YOLO: Improved YOLOv8 using optimized neck and head for product surface defect detection with data augmentation

Printed circuit board and printed circuit board assembly methods for testing and visual inspection: a review

A Comprehensive Review of Deep Learning-Based PCB Defect Detection

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