Global contextual attention augmented YOLO with ConvMixer prediction heads for PCB surface defect detection

[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 .