A Survey of PCB Defect Detection Algorithms

Lakshmi, Gayathri; Sankar, V. Udaya; Sankar, Y. Siva

doi:10.1007/s10836-023-06091-6

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2024

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Cited by 3 publications

References 40 publications

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YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

Yang,

Li,

Luo

et al. 2024

SIViP

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YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

Yang,

Li,

Luo

et al. 2024

SIViP

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Literature Review: Global Criticality Assessment Based on Feature Surrogates at the PCBA Levels

Yu,

Kamble,

Gruber

et al. 2024

2024 25th International Conference on Thermal, Mechanical and Multi-Physics Simulation and Experiments in Microelectronics and

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Soldering Defect Segmentation Method for PCB on Improved UNet

Li,

Liu

2024

Applied Sciences

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Despite being indispensable devices in the electronic manufacturing industry, printed circuit boards (PCBs) may develop various soldering defects in the production process, which seriously affect the product’s quality. Due to the substantial background interference in the soldering defect image and the small and irregular shapes of the defects, the accurate segmentation of soldering defects is a challenging task. To address this issue, a method to improve the encoder–decoder network structure of UNet is proposed for PCB soldering defect segmentation. To enhance the feature extraction capabilities of the encoder and focus more on deeper features, VGG16 is employed as the network encoder. Moreover, a hybrid attention module called the DHAM, which combines channel attention and dynamic spatial attention, is proposed to reduce the background interference in images and direct the model’s focus more toward defect areas. Additionally, based on GSConv, the RGSM is introduced and applied in the decoder to enhance the model’s feature fusion capabilities and improve the segmentation accuracy. The experiments demonstrate that the proposed method can effectively improve the segmentation accuracy for PCB soldering defects, achieving an mIoU of 81.74% and mPA of 87.33%, while maintaining a relatively low number of model parameters at only 22.13 M and achieving an FPS of 30.16, thus meeting the real-time detection speed requirements.

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A Survey of PCB Defect Detection Algorithms

Cited by 3 publications

References 40 publications

YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

YOLOv5-S-G-B: a lightweight intelligent detection model for cardboard surface defects

Literature Review: Global Criticality Assessment Based on Feature Surrogates at the PCBA Levels

Soldering Defect Segmentation Method for PCB on Improved UNet

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