PCB Defect Detection Method Based on Transformer-YOLO

Chen, Wei; Huang, Zhongtian; Mu, Qian; Sun, Yu

doi:10.1109/access.2022.3228206

Cited by 39 publications

(8 citation statements)

References 24 publications

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“…P is the precision, which indicates the probability of being correctly classified in the predicted positive sample and is calculated as shown in Eq. (17).…”

Section: Evaluation Metricsmentioning

confidence: 99%

PCB defect detection algorithm based on CDI-YOLO

Xiao,

Hou,

Zhou

2024

Sci Rep

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During the manufacturing process of printed circuit boards (PCBs), quality defects can occur, which can affect the performance and reliability of PCBs. Existing deep learning-based PCB defect detection methods are difficult to simultaneously achieve the goals of high detection accuracy, fast detection speed, and small number of parameters. Therefore, this paper proposes a PCB defect detection algorithm based on CDI-YOLO. Firstly, the coordinate attention mechanism (CA) is introduced to improve the backbone and neck network of YOLOv7-tiny, enhance the feature extraction capability of the model, and thus improve the accuracy of model detection. Secondly, DSConv is used to replace part of the common convolution in YOLOv7-tiny to achieve lower computing costs and faster detection speed. Finally, Inner-CIoU is used as the bounding box regression loss function of CDI-YOLO to speed up the bounding box regression process. The experimental results show that the method achieves 98.3% mAP on the PCB defect dataset, the detection speed is 128 frames per second (FPS), the parameters is 5.8 M, and the giga floating-point operations per second (GFLOPs) is 12.6 G. Compared with the existing methods, the comprehensive performance of this method has advantages.

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“…P is the precision, which indicates the probability of being correctly classified in the predicted positive sample and is calculated as shown in Eq. (17).…”

Section: Evaluation Metricsmentioning

confidence: 99%

PCB defect detection algorithm based on CDI-YOLO

Xiao,

Hou,

Zhou

2024

Sci Rep

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“…Unlike traditional multistage approaches, YOLO employs a single deep neural network to simultaneously predict multiple bounding boxes and their associated class probabilities for an image. This unified model, driven by its deep architecture, allows YOLO to achieve remarkable speed without compromising accuracy, making it the preferred choice for real-time object-detection tasks [25][26][27].…”

Section: A Object Detectionmentioning

confidence: 99%

Hand-Monitoring System Using CutMix-Based Synthetic Augmentation for Safety in Factories

Park,

Kim,

Ahn

2024

IEEE Access

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In this study, we introduce a robust and affordable safety monitoring system using an objectdetection algorithm trained using CutMix-based synthetic data. The image-processing algorithm, which is mainly used to improve productivity in the manufacturing industry, has recently emerged as a solution to safety problems at manufacturing sites. With deep learning, various applications have been developed to accurately monitor operator posture, wear safety equipment, and enter and exit hazardous areas. However, there are still limitations in terms of cost and time when using these applications in actual manufacturing sites, especially for small-and medium-sized enterprises. To address these challenges, our system offers the following optimal approaches: 1) leveraging domain-specific data-augmentation methods for efficient handmonitoring systems, 2) implementing the entire system with accessible hardware and software, and 3) on-site validation at an actual small-and medium-sized enterprise, demonstrating superior performance over traditional augmentation methods. Our training method achieved 98.5% accuracy using just a minute of data, outperforming traditional methods by 28.7%. The system maintains an inference rate of over eight frames per second and can be configured for less than $200. Notably, our system allows rapid training and deployment in diverse environments and has a significant impact on manufacturing safety management.

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“…Feng et al [ 9 ] developed a transformer-based deep learning model for the classification of PCBs, which utilized masked region prediction to discern relationships among different areas in the features. Chen et al [ 10 ] integrated a feature pyramid structure with the transformer as the backbone into YOLOv5 to effectively classify PCB components.…”

Section: Introductionmentioning

confidence: 99%

IC Packaging Material Identification via a Hybrid Deep Learning Framework with CNN–Transformer Bidirectional Interaction

Zhang,

Zhou,

Cai

et al. 2024

Micromachines

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With the advancement of micro- and nanomanufacturing technologies, electronic components and chips are increasingly being miniaturized. To automatically identify their packaging materials for ensuring the reliability of ICs, a hybrid deep learning framework termed as CNN–transformer interaction (CTI) model is designed on IC packaging images in this paper, in which several cascaded CTI blocks are designed to bidirectionally capture local and global features from the IC packaging image. Each CTI block involves a CNN branch with two designed convolutional neural networks (CNNs) for CNN local features and a transformer branch with two transformers for transformer global features and transformer local-window features. A bidirectional interaction mechanism is designed to interactively transfer the features in channel and spatial dimensions between the CNNs and transformers. Experimental results indicate that the hybrid framework can recognize three types of IC packaging materials with a good performance of 96.16% F1-score and 97.92% accuracy, which is superior to some existing deep learning methods.

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PCB Defect Detection Method Based on Transformer-YOLO

Cited by 39 publications

References 24 publications

PCB defect detection algorithm based on CDI-YOLO

PCB defect detection algorithm based on CDI-YOLO

Hand-Monitoring System Using CutMix-Based Synthetic Augmentation for Safety in Factories

IC Packaging Material Identification via a Hybrid Deep Learning Framework with CNN–Transformer Bidirectional Interaction

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