Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Hu, Bing; Wang, Jianhui

doi:10.1109/access.2020.3001349

Cited by 188 publications

(97 citation statements)

References 23 publications

(26 reference statements)

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“…Such a late adoption of deep learning for PCB inspection was due to various limiting characteristics in this field; for example, database acquisition is difficult and relevant companies are often reluctant to release data. Thus, research papers are often written by companies with PCB production lines [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Gang

Fabrice

Chung

et al. 2021

Sensors

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As the size of components mounted on printed circuit boards (PCBs) decreases, defect detection becomes more important. The first step in an inspection involves recognizing and inspecting characters printed on parts attached to the PCB. In addition, since industrial fields that produce PCBs can change very rapidly, the style of the collected data may vary between collection sites and collection periods. Therefore, flexible learning data that can respond to all fields and time periods are needed. In this paper, large amounts of character data on PCB components were obtained and analyzed in depth. In addition, we proposed a method of recognizing characters by constructing a dataset that was robust with various fonts and environmental changes using a large amount of data. Moreover, a coreset capable of evaluating an effective deep learning model and a base set using n-pick sampling capable of responding to a continuously increasing dataset were proposed. Existing original data and the EfficientNet B0 model showed an accuracy of 97.741%. However, the accuracy of our proposed model was increased to 98.274% for the coreset of 8000 images per class. In particular, the accuracy was 98.921% for the base set with only 1900 images per class.

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Section: Related Workmentioning

confidence: 99%

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Gang

Fabrice

Chung

et al. 2021

Sensors

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“…[21] used a Faster R-CNN to detect and locate the fiber paper tube defects. To detect the printed circuit boards, [22] also employed a Faster R-CNN architecture, coupled with a ResNet-50 for its feature extraction ability. In spite of their satisfactory performances, these solutions require a sufficient number of samples for each defect type, which is in practice hard to obtain.…”

Section: Related Workmentioning

confidence: 99%

Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN

Jalayer,

Kaboli

et al. 2021

Preprint

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A current trend in industries such as semiconductors and foundry is to shift their visual inspection processes to Automatic Visual Inspection (AVI) systems, to reduce their costs, mistakes, and dependency on human experts. This paper proposes a two-staged fault diagnosis framework for AVI systems. In the first stage, a generation model is designed to synthesize new samples based on real samples. The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor. In the second stage, an improved deep learning architecture based on Faster R-CNN, Feature Pyramid Network (FPN), and a Residual Network is proposed to perform object detection on the enhanced dataset. The performance of the algorithm is validated and evaluated on two multi-class datasets. The experimental results performed over a range of imbalance severities demonstrate the superiority of the proposed framework compared to other solutions.

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“…It improved the performance of the detector. Hu et al [24] used a ResNet50 [25] with FPN as the backbone for feature extraction, which achieved a learning-based image detection method for printed circuit board defect detection.…”

Section: A Optimal Fpn Featurementioning

confidence: 99%

ReFPN-FCOS: One-Stage Object Detection for Feature Learning and Accurate Localization

Zeng

Xiong

et al. 2020

IEEE Access

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Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network

Cited by 188 publications

References 23 publications

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Character Recognition of Components Mounted on Printed Circuit Board Using Deep Learning

Automatic Visual Inspection of Rare Defects: A Framework based on GP-WGAN and Enhanced Faster R-CNN

ReFPN-FCOS: One-Stage Object Detection for Feature Learning and Accurate Localization

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