[
1. Ribeiro, A. C. M., R. C. Scharlach, M. M. C. Pinheiro. Assessment of Temporal Aspects in Popular Singers. – CODAS, Vol. 27, 2015. https://doi.org/10.1590/2317-1782/2015201423410.1590/2317-1782/2015201423426691615
]Search in Google Scholar
[
2. Bai, T., Y. Pang, J. Wang, K. Han, J. Luo, H. Wang, J. Lin, J. Wu, H. Zhang. An Optimized Faster R-CNN Method Based on DRNet and RoI Align for Building Detection in Remote Sensing Images. – Remote Sens., Vol. 12, 2020. https://doi.org/10.3390/rs1205076210.3390/rs12050762
]Search in Google Scholar
[
3. Wetzel, J., A. Laubenheimer, M. Heizmann. Joint Probabilistic People Detection in Overlapping Depth Images. – IEEE Access, Vol. 8, 2020. https://doi.org/10.1109/ACCESS.2020.297205510.1109/ACCESS.2020.2972055
]Search in Google Scholar
[
4. Dewi, C., R. C. Chen, H. Yu. Weight Analysis for Various Prohibitory Sign Detection and Recognition Using Deep Learning. Multimed. – Tools Appl. Vol. 79, 2020, pp. 32897-32915. https://doi.org/10.1007/s11042-020-09509-x10.1007/s11042-020-09509-x
]Search in Google Scholar
[
5. Xi, X., Z. Yu, Z. Zhan, Y. Yin, C. Tian. Multi-Task Cost-Sensitive-Convolutional Neural Network for Car Detection. – IEEE Access, Vol. 7, 2019. https://doi.org/10.1109/ACCESS.2019.292786610.1109/ACCESS.2019.2927866
]Search in Google Scholar
[
6. Dewi, C., R. C. Chen, Y. T. Liu. Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image Generation. – In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, pp. 479-493. https://doi.org/10.1007/978-3-030-73280-6_3810.1007/978-3-030-73280-6_38
]Search in Google Scholar
[
7. Ju, M., S. Moon, C. D. Yoo. Object Detection for Similar Appearance Objects Based on Entropy. – In: Proc. of 7th International Conference on Robot Intelligence Technology and Applications (RiTA’19), 2019. https://doi.org/10.1109/RITAPP.2019.893279110.1109/RITAPP.2019.8932791
]Search in Google Scholar
[
8. Jiang, Y., L. Chen, H. Zhang, X. Xiao. Breast Cancer Histopathological Image Classification Using Convolutional Neural Networks with Small SE-ResNet Module. – PLoS One, Vol. 14, 2019. https://doi.org/10.1371/journal.pone.021458710.1371/journal.pone.0214587644062030925170
]Search in Google Scholar
[
9. Yu, X., C. Kang, D. S. Guttery, S. Kadry, Y. Chen, Y. D. Zhang. ResNet-SCDA-50 for Breast Abnormality Classification. IEEE/ACM Trans. – Comput. Biol. Bioinforma, Vol. 18, 2021. https://doi.org/10.1109/TCBB.2020.298654410.1109/TCBB.2020.298654432287004
]Search in Google Scholar
[
10. Yao, B., L. Fei-Fei. Grouplet: A Structured Image Representation for Recognizing Human and Object Interactions. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. https://doi.org/10.1109/CVPR.2010.554023410.1109/CVPR.2010.5540234
]Search in Google Scholar
[
11. Zhang, X., F. Wan, C. Liu, X. Ji, Q. Ye. Learning to Match Anchors for Visual Object Detection. – IEEE Trans. Pattern Anal. Mach. Intell., 2021. https://doi.org/10.1109/TPAMI.2021.305049410.1109/TPAMI.2021.305049433434120
]Search in Google Scholar
[
12. Girshick, R. Fast R-CNN. – In: Proc. of IEEE International Conference on Computer Vision, 2015, pp. 1440-1448. https://doi.org/10.1109/ICCV.2015.16910.1109/ICCV.2015.169
]Search in Google Scholar
[
13. Cheng, G., Y. Si, H. Hong, X. Yao, L. Guo. Cross-Scale Feature Fusion for Object Detection in Optical Remote Sensing Images. – IEEE Geosci. Remote Sens. Lett., Vol. 18, 2021. https://doi.org/10.1109/LGRS.2020.297554110.1109/LGRS.2020.2975541
]Search in Google Scholar
[
14. Redmon, J., S. Divvala, R. Girshick, A. Farhadi. You Only Look Once: Unified, Real-Time Object Detection. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, pp. 779-788. https://doi.org/10.1109/CVPR.2016.9110.1109/CVPR.2016.91
]Search in Google Scholar
[
15. Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C. Y. Fu, A. C. Berg. SSD: Single Shot Multibox Detector. – In: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2016, pp. 21-37. https://doi.org/10.1007/978-3-319-46448-0_210.1007/978-3-319-46448-0_2
]Search in Google Scholar
[
16. Srinivasan, K., P. Balamurugan, V. R. Azhaguramyaa. Survey on Similar Object Detection in H.264 Compressed Video. – In: Proc. of 2017 International Conference on Algorithms, Methodology, Models and Applications in Emerging Technologies (ICAMMAET’17), 2017. https://doi.org/10.1109/ICAMMAET.2017.818666310.1109/ICAMMAET.2017.8186663
]Search in Google Scholar
[
17. Grauman, K., T. Darrell. The Pyramid Match Kernel: Discriminative Classification with Sets of Image Features. – In: Proc. of IEEE International Conference on Computer Vision, 2005, pp. 1458-1465. https://doi.org/10.1109/ICCV.2005.23910.1109/ICCV.2005.239
]Search in Google Scholar
[
18. Lazebnik, S., C. Schmid, J. Ponce. Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006, pp. 1-8. https://doi.org/10.1109/CVPR.2006.6810.1109/CVPR.2006.68
]Search in Google Scholar
[
19. Dai, J., Y. Li, K. He, J. Sun. R-FCN: Object Detection via Region-Based Fully Convolutional Networks. – In: Advances in Neural Information Processing Systems, 2016, pp. 379-387.
]Search in Google Scholar
[
20. Sivic, J., A. Zisserman. Video Google: A Text Retrieval Approach to Object Matching in Videos. – In: Proc. of IEEE International Conference on Computer Vision, 2003, pp. 1-8. https://doi.org/10.1109/iccv.2003.123866310.1109/ICCV.2003.1238663
]Search in Google Scholar
[
21. Yang, J., K. Yu, Y. Gong, T. Huang. Linear Spatial Pyramid Matching Using Sparse Coding for Image Classification. – In: Proc. of 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, 2009, pp. 1794-1801. https://doi.org/10.1109/CVPRW.2009.520675710.1109/CVPR.2009.5206757
]Search in Google Scholar
[
22. Wang, J., J. Yang, K. Yu, F. Lv, T. Huang, Y. Gong. Locality-Constrained Linear Coding for Image Classification. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, pp. 3360-3367. https://doi.org/10.1109/CVPR.2010.554001810.1109/CVPR.2010.5540018
]Search in Google Scholar
[
23. Van de Sande, K. E. A., J. R. R. Uijlings, T. Gevers, A. W. M. Smeulders. Segmentation as Selective Search for Object Recognition. – In: Proc. of IEEE International Conference on Computer Vision, 2011, pp. 1879-1886. https://doi.org/10.1109/ICCV.2011.612645610.1109/ICCV.2011.6126456
]Search in Google Scholar
[
24. He, K., X. Zhang, S. Ren, J. Sun. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition. – IEEE Trans. Pattern Anal. Mach. Intell., Vol. 37, 2015, pp. 1904-1916. https://doi.org/10.1109/TPAMI.2015.238982410.1109/TPAMI.2015.238982426353135
]Search in Google Scholar
[
25. He, K., X. Zhang, S. Ren, J. Sun. Deep Residual Learning for Image Recognition. – In: Proc. of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, pp. 770-778. https://doi.org/10.1109/CVPR.2016.9010.1109/CVPR.2016.90
]Search in Google Scholar
[
26. Chander, G., B. L. Markham, D. L. Helder. Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. – Remote Sens. Environ., Vol. 113, 2009, pp. 893-903. https://doi.org/10.1016/j.rse.2009.01.00710.1016/j.rse.2009.01.007
]Search in Google Scholar
[
27. Fang, W., C. Wang, X. Chen, W. Wan, H. Li, S. Zhu, Y. Fang, B. Liu, Y. Hong. Recognizing Global Reservoirs from Landsat 8 Images: A Deep Learning Approach. – IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., Vol. 12, 2019, pp. 3168-3177. https://doi.org/10.1109/jstars.2019.292960110.1109/JSTARS.2019.2929601
]Search in Google Scholar
[
28. Ibrahim, Y., H. Wang, M. Bai, Z. Liu, J. Wang, Z. Yang, Z. Chen. Soft Error Resilience of Deep Residual Networks for Object Recognition. – IEEE Access, Vol. 8, 2020, pp. 19490-19503. https://doi.org/10.1109/ACCESS.2020.296812910.1109/ACCESS.2020.2968129
]Search in Google Scholar
[
29. Wen, L., X. Li, L. Gao. A Transfer Convolutional Neural Network for Fault Diagnosis Based on ResNet-50. – Neural Comput. Appl., Vol. 32, 2020. https://doi.org/10.1007/s00521-019-04097-w10.1007/s00521-019-04097-w
]Search in Google Scholar
[
30. Fulton, L. V., D. Dolezel, J. Harrop, Y. Yan, C. P. Fulton. Classification of Alzheimer’s Disease with and without Imagery Using Gradient Boosted Machines and Resnet-50. – Brain Sci., Vol. 9, 2019. https://doi.org/10.3390/brainsci909021210.3390/brainsci9090212677093831443556
]Search in Google Scholar
[
31. Dewi, C., R.-C. Chen, Y.-T. Liu, S.-K. Tai. Synthetic Data Generation Using DCGAN for Improved Traffic Sign Recognition. – Neural Comput. Appl., Vol. 33, 2021, pp. 1-15.10.1007/s00521-021-05982-z
]Search in Google Scholar
[
32. Arcos-García, Á., J. A. Álvarez-García, L. M. Soria-Morillo. Evaluation of Deep Neural Networks for Traffic Sign Detection Systems. – Neurocomputing., Vol. 316, 2018, pp. 332-344. https://doi.org/10.1016/j.neucom.2018.08.00910.1016/j.neucom.2018.08.009
]Search in Google Scholar
[
33. Dewi, C., R. C. Chen, H. Yu, X. Jiang. Robust Detection Method for Improving Small Traffic Sign Recognition Based on Spatial Pyramid Pooling. – J. Ambient Intell. Humaniz. Comput., Vol. 12, 2021. https://doi.org/10.1007/s12652-021-03584-010.1007/s12652-021-03584-0
]Search in Google Scholar
[
34. Yang, H., L. Chen, M. Chen, Z. Ma, F. Deng, M. Li, X. Li. Tender Tea Shoots Recognition and Positioning for Picking Robot Using Improved YOLO-V3 Model. – IEEE Access., Vol. 7, 2019, pp. 180998-181011. https://doi.org/10.1109/ACCESS.2019.295861410.1109/ACCESS.2019.2958614
]Search in Google Scholar
[
35. Tian, Y., G. Yang, Z. Wang, H. Wang, E. Li, Z. Liang. Apple Detection During Different Growth Stages in Orchards Using the Improved YOLO-V3 Model. – Comput. Electron. Agric., Vol. 157, 2019, pp. 417-426. https://doi.org/10.1016/j.compag.2019.01.01210.1016/j.compag.2019.01.012
]Search in Google Scholar