Improved YOLOv3 detection method for PCB plug-in solder joint defects based on ordered probability density weighting and attention mechanism

Abstract

Printed Circuit Board (PCB) is the heart component of electronic products, and its defect detection is the basic requirement of PCB quality control in the production process. Traditional visual detection methods need artificial design features, so their detection accuracy is poor, and the rate of missed and false detection is high. To solve the above problems, this paper proposes an improved YOLOv3 (You Only Look Once) detection method for PCB plug-in solder spot defects based on the combination of the ordered probability density weighting and the attention mechanism. First, in order to obtain a higher priority priori box, the ordered probability density weighting (OWA) method is used to optimize the multiple sets of a priori boxes generated by K-means. Then, to get more effective feature information, the Squeeze-and-Excitation mechanism (SE) is added to the backbone network. In the feature detection network, the Convolutional Block Attention Module (CBAM) attention mechanism is joined, at the same time in the inspection network output layer three layer feature are fusions. Finally, in order to accelerate the convergence speed of model and improve the accuracy of the model, the network loss function was improved by using the generalized joint generalized intersection over union (GIoU), and the COCO data model was applied to PCB solder spot defect training by transfer learning method. After testing, the average detection accuracy of improved network is improved from 84.35% to 96.69%, and the improved network has better convergence than the original network. The study shows that the improved method based on YOLOv3 is more suitable for industrial application of PCB plug-in solder spot defect detection.

Keywords

PCB plug-in welding spot defect detection YOLOv3 ordered probability density weighting attention mechanism

Get full access to this article

View all access options for this article.

References

Ardhy and

F.I.

Hariadi, Development of SBC based machine-vision system for PCB board assembly automatic optical inspection, in: 2016 International Symposium on Electronics and Smart Devices (ISESD), IEEE, 2016, pp. 386–393. doi:10.1109/ISESD.2016.7886753.

V.H.

Gaidhane,

Y.V.

Hote and

Singh, An efficient similarity measure approach for PCB surface defect detection, Pattern Analysis & Applications 21(1) (2018), 277–289. doi:10.1007/s10044-017-0640-9.

Girshick, Fast R-CNN, in: Proceedings of the IEEE International Conference on Computer Vision, IEEE, 2015, pp. 1440–1448.

Girshick,

Donahue,

Darrell et al., Rich feature hierarchies for accurate object detection and semantic segmentation, in: IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2014, pp. 580–587.

Goodfellow,

Bengio and

Courville, Deep Learning, MIT Press, 2016.

He,

Zhang,

Ren et al., Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis & Machine Intelligence 37(9) (2015), 1904–1916. doi:10.1109/TPAMI.2015.2389824.

Ioffe and

Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, in: International Conference on Machine Learning, 2015, pp. 448–456.

Jingyi,

Wentao,

Xiaomin et al., Research progress of PCB surface defect detection methods based on machine vision, Journal of Zhejiang Sci-Tech University 45(3) (2021), 379–389.

Kaiming,

Xiang,

Shanqing and

Jian, Deep residual learning for image, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778.

10.

Li,

Liu,

Zhang et al., Object detection based on an adaptive attention mechanism, Scientific Reports 10(1) (2020), 1–13. doi:10.1038/s41598-018-33214-3.

11.

Li,

Du et al., YOLO-ACN: Focusing on small target and occluded object detection, IEEE Access 8 (2020), 227288–227303. doi:10.1109/ACCESS.2020.3046515.

12.

Li,

Zhao and

Pan, Lightweight ship detection methods based on YOLOv3 and DenseNet, Mathematical Problems in Engineering 2020.

13.

T.Y.

Lin,

Dollar,

Girshick et al., Feature pyramid networks for object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017, pp. 2117–2125.

14.

Liu,

Anguelov,

Erhan et al., SSD: Single shot MultiBox detector, in: European Conference on Computer Vision, Springer, Cham, 2016.

15.

Ma,

Liu,

Ren et al., Detection of collapsed buildings in post-earthquake remote sensing images based on the improved YOLOv3, Remote Sensing 12(1) (2019), 44. doi:10.3390/rs12010044.

16.

Ma, Defect detection and recognition of bare PCB based on computer vision, in: 2017 36th Chinese Control Conference (CCC), IEEE, 2017.

17.

Ma,

Zhang,

H.T.

Zheng et al., ShuffleNet V2: Practical guidelines for efficient CNN architecture design, in: Proceedings of the European Conference on Computer Vision, Springer, 2018, pp. 116–131.

18.

A.L.

Maas,

A.Y.

Hannun and

A.Y.

Ng, Rectifier nonlineari-ties improve neural network acoustic models, Proc 30(1) (2013), 111–120.

19.

Najibi,

Rastegari and

L.S.

Davis, G-CNN: An iterative grid based object detector, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016, pp. 2369–2377.

20.

Rajchl,

Lee,

Oktay et al., DeepCut: Object segmentation from bounding box annotations using convolutional neural networks, IEEE Transactions on Medical Imaging 336(2) (2016), 674–683. doi:10.1109/TMI.2016.2621185.

21.

Redmon,

Divvala,

Girshick et al., You only look once: Unified, real-time object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2016, pp. 779–788.

22.

Redmon and

Farhadi, YOLO9000: Better, Faster, Stronger,in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2017, pp. 6517–6525.

23.

Redmon and

Farhadi, YOLOv3: An Incremental Improvement, arXiv e-prints (2018), 1–6.

24.

Ren,

He,

Girshick and

Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, Advances in neural information processing systems 39(6) (2017), 1137–1149.

25.

J.R.R.

Uijlings,

K.E.A.

Van De Sande,

Gevers et al., Selective search for object recognition, International Journal of Computer Vision 104(2) (2013), 154–171. doi:10.1007/s11263-013-0620-5.

26.

Wang,

Chen,

Yang et al., Region proposal by guided anchoring, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2019, pp. 2965–2974.

27.

Wang,

Liu and

Ye, An advanced YOLOv3 method for small-scale road object detection, Applied Soft Computing 112 (2021), 107846. doi:10.1016/j.asoc.2021.107846.

28.

Wang and

Fan, Comparative analysis of image classification algorithms based on traditional machine learning and deep learning, Pattern Recognition Letters 141(11) (2020), 61–67.

29.

Xu,

Lin,

Yue et al., Research on small target detection in driving scenarios based on improved yolo network, IEEE Access 8 (2020), 27574–27583. doi:10.1109/ACCESS.2020.2966328.

30.

Zhang,

C.C.

Chang and

Jamshidi, Bridge Damage Detection using a Single-Stage Detector and Field Inspection Images, 2018. arXiv:1812.10590.

31.

Zhang,

Wen,

Bian,

Lei and

Li, Single-shot refifinement neural network for object detection, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 4203–4212.

32.

Zhang,

Gao,

Wang et al., Improve YOLOv3 using dilated spatial pyramid module for multi-scale object detection, International Journal of Advanced Robotic Systems 17(4) (2020), 1729881420936062.