The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network
Open accessAbstract
Defect detection is an essential requirement for quality control in the production of printed circuit boards (PCBs) manufacturing. The traditional defect detection methods have various drawbacks, such as strongly depending on a carefully designed template, highly computational cost, and noise-susceptibility, which pose a significant challenge in a production environment. In this paper, a deep learning-based image detection method for PCB defect detection is proposed. This method builds a new network based on Faster RCNN. We use a ResNet50 with Feature Pyramid Networks as the backbone for feature extraction, to better detect small defects on the PCB. Secondly, we use GARPN to predict more accurate anchors and merge the residual units of ShuffleNetV2. The experimental results show that this method is more suitable for use in production than other PCB defect detection methods. We have also tested in other PCB defects dataset, and experiments have shown that this method is equally valid.
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Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Focal Loss for Dense Object Detection
Lin, T.-Y., Goyal, P., Girshick, R., He, K., & Dollar, P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.324 · Cited by 30,581
Xception: Deep Learning with Depthwise Separable Convolutions
Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_8 · Cited by 7,356
Cited by
Zeng, N., Wu, P., Wang, Z., Li, H., Liu, W., & Liu, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3153997 · Cited by 523
Surface Defect Detection Methods for Industrial Products: A Review
Chen, Y., Ding, Y., Zhao, F., Zhang, E., Wu, Z., & Shao, L. (2021) · Applied Sciences · doi:10.3390/app11167657 · Cited by 457
A Lightweight YOLOv8 Tomato Detection Algorithm Combining Feature Enhancement and Attention
Yang, G., Wang, J., Nie, Z., Yang, H., & Yu, S. (2023) · Agronomy · doi:10.3390/agronomy13071824 · Cited by 323
Self-citationPCB defect detection algorithm based on CDI-YOLO
Xiao, G., Hou, S., & Zhou, H. (2024) · Scientific Reports · doi:10.1038/s41598-024-57491-3 · Cited by 113
Metadata
- Authors
- Hu, B., & Wang, J.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 108335-108345
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3034713821
- Cited by
- 340
- References
- 7
- FWCI
- 20.14
- Type
- journal article
- Language
- en
- Open access
- gold
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