Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Qin, Lihua; Isa, Nor Ashidi Mat

doi:10.1109/access.2023.3245093

Cited by 38 publications

(26 citation statements)

References 105 publications

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“…The field of image processing has a broad range of research directions. One area of study is defect detection, as explored by [22]. In the medical field, image processing has been utilized for a variety of purposes, such as in [43,6,31,9].…”

Section: Related Workmentioning

confidence: 99%

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

Gao

2023

ITC

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In deep learning, model quality is extremely important. Consequently, the quality and the sufficiency of the datasets for training models have attracted considerable attention from both industry and academia. Automatic data augmentation, which provides a means of using image processing operators to generate data from existing datasets, is quite effective in searching for mutants of the images and expanding the training datasets. However, existing automatic data augmentation techniques often fail to fully exploit the potential of the data, failing to balance the search efficiency and the model accuracy. This paper presents CAugment, a novel approach to diversifying image datasets by combining image processing operators. Given a training image dataset, CAugment is composed of: 1) the three-level evolutionary algorithm (TLEA) that employs three levels of atomic operations for augmenting the dataset and an adaptive strategy for decreasing granularity and 2) a design that uses the three-dimensional evaluation method (TDEM) and a dHash algorithm to measure the diversity of the dataset. The search space can be expanded, which further improves model accuracy during training. We use CAugment to augment the CIFAR-10/100 and SVHN datasets and use the augmented datasets to train the WideResNet and Shake-Shake models. Our results show that the amount of data increases linearly along with the training epochs; in addition, the models trained by the CAugment-augmented datasets outperform those trained by the datasets augmented by the other techniques by up to 17.9% in accuracy on the SVHN dataset.

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Section: Related Workmentioning

confidence: 99%

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

Gao

2023

ITC

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“…Industrial internet is the core cornerstone of the fourth industrial revolution [1], so in the field of electronics manufacturing, printed circuit board (PCB) as a variety of components connected to the important parts [2], its quality plays a vital role in the quality of electronic products integrated by it. In recent years, the density and complexity of PCB have been increasing, and the production process of PCB is also gradually becoming more cumbersome.…”

Section: Introductionmentioning

confidence: 99%

DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Xu,

Huo

2024

Electronics

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Printed circuit board (PCB) defect detection is an important and indispensable part of industrial production. PCB defects, due to the small target and similarity between classes, in the actual production of the detection process are prone to omission and false detection problems. Traditional machine-learning-based detection methods are limited by the actual needs of industrial defect detection and do not show good results. Aiming at the problems related to PCB defect detection, we propose a PCB defect detection algorithm based on DSASPP-YOLOv5 and conduct related experiments on the PKU-Market-PCB dataset. DSASPP-YOLOv5 is an improved single-stage detection model, and we first used the K-means++ algorithm for the PKU-Market-PCB dataset to recluster the model so that the model is more in line with the characteristics of PCB small target defects. Second, we design the Depthwise Separable Atrous Spatial Pyramid Pooling (DSASPP) module, which effectively improves the correlation between local and global information by constructing atrous convolution branches with different dilated rates and a global average pooling branch. The experimental results show that our model achieves satisfactory results in both the mean average precision and detection speed metrics compared to existing models, proving the effectiveness of the proposed method.

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“…Multilayer printed circuit boards (PCBs) are an important part of multiple devices [ 1 ]. Today, PCBs can be produced not only in the traditional ways, such as layering, photolithography, drilling, and plating, but with recent advances in 3D printing technologies, they can also be produced additively [ 2 , 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…PCBs that are part of electronic components in, i.e., military devices, should be reliable and cannot fail unexpectedly [ 1 ]. No matter what kind of technology is used for the production of a PCB, when they are used in important structures, their quality has to be ensured.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Different NDT Techniques for Evaluation of the Quality of PCBs Produced Using Traditional vs. Additive Manufacturing Technologies

Jasiūnienė,

Raišutis,

Samaitis

et al. 2024

Sensors

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Multilayer printed circuit boards (PCBs) can be produced not only in the traditional way but also additively. Both traditional and additive manufacturing can lead to invisible defects in the internal structure of the electronic component, eventually leading to the spontaneous failure of the device. No matter what kind of technology is used for the production of PCBs, when they are used in important structures, quality control is important to ensure the reliability of the component. The nondestructive testing (NDT) of the structure of manufactured electronic components can help ensure the quality of devices. Investigations of possible changes in the structure of the product can help identify the causes of defects. Different types of manufacturing technologies can lead to diverse types of possible defects. Therefore, employing several nondestructive inspection techniques could be preferable for the inspection of electronic components. In this article, we present a comparison of various NDT techniques for the evaluation of the quality of PCBs produced using traditional and additive manufacturing technologies. The methodology for investigating the internal structure of PCBs is based on several of the most reliable and widely used technologies, namely, acoustic microscopy, active thermography, and radiography. All of the technologies investigated have their advantages and disadvantages, so if high-reliability products are to be produced, it would be advantageous to carry out tests using multiple technologies in order to detect the various types of defects and determine their parameters.

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Printed Circuit Board Defect Detection Methods Based on Image Processing, Machine Learning and Deep Learning: A Survey

Cited by 38 publications

References 105 publications

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

CAugment: An Approach to Diversifying Dataset by Combining Image Processing Operations

DSASPP: Depthwise Separable Atrous Spatial Pyramid Pooling for PCB Surface Defect Detection

Comparison of Different NDT Techniques for Evaluation of the Quality of PCBs Produced Using Traditional vs. Additive Manufacturing Technologies

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