Mobile QR Code QR CODE

REFERENCES

1 
Xuebin Q., Zichen Z., Chenyang H., Martin J., Aug. 2020., U2-Net: Going deeper with nested U-structure for salient object detection, Pattern Recognition, Vol. 106DOI
2 
Taylor L., Geoff N., Nov. 2018., Improving deep learning with generic data augmentation, IEEE Symposium Series on Computational Intelligence, pp. 1542-1547DOI
3 
Zhang H., Moustapha C., Yann N D., David L., Oct. 2017. , mixup: Beyond empirical risk minimization, arXiv preprint arXiv:1710.09412DOI
4 
Takahashi R., Takashi M., Kuniaki U., 2019, Data augmentation using random image cropping and patching for deep CNNs, IEEE Transactions on Circuits and Systems for Video Technology 30.9, Vol. 30, No. 9, pp. 2917-2931DOI
5 
Summers C., Michael J., Dinneen , Mar. 2019, Improved mixed-example data augmentation, Winter Conference on Applications of Computer Vision, pp. 1262-1270DOI
6 
Zhong Z., Liang Z., Guoliang K., Yi Y., 2020, Random erasing data augmentation, Proceedings of the AAAI conference on artificial intelligence, Vol. 34, No. 7, pp. 13001-13008DOI
7 
DeVries T., Graham W., Nov. 2017, Improved regularization of convolutional neural networks with cutout, arXiv preprint arXiv:1708.04552DOI
8 
Sangdoo Y., Dongyoon H., Seong joon O., Junsuk C., 2019, cutmix: Regularization strategy to train strong classifiers with localizable features, Proceedings of the IEEE/CVF international conference on computer vision, pp. 6023-6032DOI
9 
Creswell A., Tom W., Vincent D., Kai A., Jan. 2018, Generative adversarial networks: An overview, IEEE Signal Processing Magazine 35.1, Vol. 35, No. 1, pp. 53-65DOI
10 
Tanaka F., Henrique K., Claus Aranha , Apr. 2019, Data augmentation using GANs, arXiv preprint arXiv:1904.09135DOI
11 
Remez T., Huang J., Brown M., 2018, Learning to segment via cut-and-paste, in Proc. ECCV, pp. 37-52DOI
12 
Dwibedi D., Ishan M., Martial Hebert , 2017, cut, paste and learn: Surprisingly easy synthesis for instance detection, Proceedings of the IEEE international conference on computer vision, pp. 1301-1310DOI
13 
Tripathi S., Siddhartha C., Amit A., Ambrish T., 2019, Learning to generate synthetic data via compositing, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 461-470DOI
14 
Sengdeok B., Francis B., Somin P., Wontae K., Jul. 2020, Image augmentation to improve construction resource detection using generative adversarial networks, cut and paste, image transformation techniques, Automation in Construction 115, Vol. 115DOI
15 
Hummel , Robert A., Kimia B., Steven W., 1987, Deblurring gaussian blur, Computer Vision, Graphics, Image Processing 38.1, Vol. 38, No. 1, pp. 66-80DOI
16 
Mariani G., Florian S., Roxana I., Costas B., Jun. 2018, Bagan: Data augmentation with balancing gan, arXiv preprint arXiv:1803.09655DOI
17 
Tellez D., Geert L., B. Peter B, Dec. 2019, Wouter. Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology, Medical image analysis 58, Vol. 58DOI
18 
Hwang S., Minsong K., Seung L., Sanghoon P., 2022, cut and Continuous paste towards Real-time Deep Fall Detection, arXiv preprint arXiv: 2202.10687, pp. 1775-1779DOI
19 
Bochkovskiy A., Chien-Yao Wang , Hong-Yuan M., Apr. 2020, Yolov4: Optimal speed and accuracy of object detection, arXiv preprint arXiv: 2004.10934DOI
20 
Zhou F., Huailin Z., Zhen N., 2021, Safety helmet detection based on YOLOv5, International Conference on Power Electronics, Computer Applications, pp. 6-11DOI
21 
Tan M., Ruoming P., Quoc LE V., 2020, Efficientdet: Scalable and efficient object detection, Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp. 10781-10790DOI