Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection
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Ke-wen Xia | C. Zhou | Zhongliang Lv | Kang Liu | Zhenyu Lu | Hong Zhu | Xuanlin Chen
[1] Wanghui Zou,et al. PCB-YOLO: An Improved Detection Algorithm of PCB Surface Defects Based on YOLOv5 , 2023, Sustainability.
[2] Hang Xiao,et al. Light-YOLOv5: A Lightweight Algorithm for Improved YOLOv5 in PCB Defect Detection , 2023, 2023 IEEE 2nd International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA).
[3] Jigang Wu,et al. Research on PCB Small Target Defect Detection Based on Improved YOLOv5 , 2022, 2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD).
[4] Mingde Zhao,et al. Research on PCB defect detection based on SSD , 2022, 2022 IEEE 4th International Conference on Civil Aviation Safety and Information Technology (ICCASIT).
[5] S. Cloutier,et al. End-to-end deep learning framework for printed circuit board manufacturing defect classification , 2022, Scientific Reports.
[6] Guihua Liu,et al. ESDDNet: efficient small defect detection network of workpiece surface , 2022, Measurement Science and Technology.
[7] Mukhil Azhagan Mallaiyan Sathiaseelan,et al. Deep Learning-Based Approaches for Text Recognition in PCB Optical Inspection: A Survey , 2021, 2021 IEEE Physical Assurance and Inspection of Electronics (PAINE).
[8] Qi Zhao,et al. TPH-YOLOv5: Improved YOLOv5 Based on Transformer Prediction Head for Object Detection on Drone-captured Scenarios , 2021, 2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW).
[9] Fengzhou Fang,et al. State of the Art in Defect Detection Based on Machine Vision , 2021, International Journal of Precision Engineering and Manufacturing-Green Technology.
[10] A. Yuille,et al. DetectoRS: Detecting Objects with Recursive Feature Pyramid and Switchable Atrous Convolution , 2020, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[11] Kunihito Kato,et al. Adversarial autoencoder for detecting anomalies in soldered joints on printed circuit boards , 2020, J. Electronic Imaging.
[12] Thomas G. Dietterich. What is machine learning? , 2015, Archives of Disease in Childhood.
[13] Kai Feng,et al. A Novel Multi-Pattern Solder Joint Simultaneous Segmentation Algorithm for PCB Selective Packaging Systems , 2019, Int. J. Pattern Recognit. Artif. Intell..
[14] Shifeng Zhang,et al. Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[15] Jun-Wei Hsieh,et al. CSPNet: A New Backbone that can Enhance Learning Capability of CNN , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW).
[16] Quoc V. Le,et al. EfficientDet: Scalable and Efficient Object Detection , 2019, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[17] Fathi E. Abd El-Samie,et al. A real-time approach for automatic defect detection from PCBs based on SURF features and morphological operations , 2019, Multimedia Tools and Applications.
[18] Stephen Lin,et al. RepPoints: Point Set Representation for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[19] Guangpeng Li,et al. TDD-net: a tiny defect detection network for printed circuit boards , 2019, CAAI Trans. Intell. Technol..
[20] Qi Tian,et al. CenterNet: Keypoint Triplets for Object Detection , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).
[21] Hong Liu,et al. Defect detection of PCB based on Bayes feature fusion , 2018, The Journal of Engineering.
[22] Shu Liu,et al. Path Aggregation Network for Instance Segmentation , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[23] Dimitrios Tzovaras,et al. A framework for inspection of dies attachment on PCB utilizing machine learning techniques , 2018 .
[24] Vijander Singh,et al. An efficient similarity measure approach for PCB surface defect detection , 2018, Pattern Analysis and Applications.
[25] Lukasz Kaiser,et al. Attention is All you Need , 2017, NIPS.
[26] Serge J. Belongie,et al. Feature Pyramid Networks for Object Detection , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[27] Jianxiong Xiao,et al. R-CNN for Small Object Detection , 2016, ACCV.
[28] Abhinav Gupta,et al. Contextual Priming and Feedback for Faster R-CNN , 2016, ECCV.
[29] Rogério Schmidt Feris,et al. A Unified Multi-scale Deep Convolutional Neural Network for Fast Object Detection , 2016, ECCV.
[30] Wei Liu,et al. SSD: Single Shot MultiBox Detector , 2015, ECCV.
[31] Erwan Scornet,et al. A random forest guided tour , 2015, TEST.
[32] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[33] Ross B. Girshick,et al. Fast R-CNN , 2015, 1504.08083.
[34] Sotiris B. Kotsiantis,et al. Decision trees: a recent overview , 2011, Artificial Intelligence Review.
[35] Antonio Torralba,et al. Ieee Transactions on Pattern Analysis and Machine Intelligence 1 80 Million Tiny Images: a Large Dataset for Non-parametric Object and Scene Recognition , 2022 .
[36] William Stafford Noble,et al. Support vector machine , 2013 .
[37] D. Tsai,et al. Defect Detection of Gold-Plated Surfaces on PCBs Using Entropy Measures , 2002 .
[38] Weibo Liu,et al. A Small-Sized Object Detection Oriented Multi-Scale Feature Fusion Approach With Application to Defect Detection , 2022, IEEE Transactions on Instrumentation and Measurement.
[39] Liangxiao Jiang,et al. CS-ResNet: Cost-sensitive residual convolutional neural network for PCB cosmetic defect detection , 2021, Expert Syst. Appl..
[40] Jianhui Wang,et al. Detection of PCB Surface Defects With Improved Faster-RCNN and Feature Pyramid Network , 2020, IEEE Access.
[41] Du-Ming Tsai,et al. Defect Detection in Electronic Surfaces Using Template-Based Fourier Image Reconstruction , 2019, IEEE Transactions on Components, Packaging and Manufacturing Technology.
[42] Р Ю Чуйков,et al. Обнаружение транспортных средств на изображениях загородных шоссе на основе метода Single shot multibox Detector , 2017 .