شناسایی و تشخیص اشیاء در تصویر با استفاده از الگوریتم یولو بهینه شده

[1]     A. Abdullah, G. Mohammad Amin, and Sh. F. Mohammad Hassan, "Diagnosis of road slippage using road camera images based on convolutional neural networks and transfer learning," 2022, Accessed: Mar. 12, 2024. https://www.sid.ir/paper/1082685/fa(In Persian)

[2]     M.Asadi, S.Parsa, M. Jamali, and V.Majidinjad, "Peer-to-Peer Bot Network Detection Using Deep Learning Method," Electronic and Cyber Defense, vol. 8, no. 2, pp. 1–14, 2020. (In Persian). https://dor.isc.ac/dor/20.1001.1.23224347.1399.8.2.1.3

[3]     A. Niloufer, M. M. Seyyed Mohammad Reza, and T. A. Sadat, "Reducing the effects of deception attacks in GPS receivers of phasor measurement units using neural networks," 2023, Accessed: Mar. 12, 2024. [Online]. Available: https://www.sid.ir/paper/1083027/fa. (In Persian)

[4]     C. Szegedy, A. Toshev, and D. Erhan, “Deep neural networks for object detection,” Advances in neural information processing systems, vol. 26, 2013, Accessed: Nov. 13, 2023. [Online]. Available: https://proceedings.neurips.cc/paper/5207-deep-neural-networks-for-object-detection

[5]     E. N. Mortensen, H. Deng, and L. Shapiro, “A SIFT descriptor with global context,” in 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR’05), IEEE, 2005, pp. 184–190. Accessed: Nov. 13, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/1467266

[6]     X. Wang, T. X. Han, and S. Yan, “An HOG-LBP human detector with partial occlusion handling,” in 2009 IEEE 12th international conference on computer vision, IEEE, 2009, pp. 32–39. Accessed: Nov. 13, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/5459207

[7]     A. Zanganeh and M. Jampour, “Automatic Weak Learners Selection for Pattern Recognition and its application in Soccer Goal Recognition,” in 2019 4th International Conference on Pattern Recognition and Image Analysis (IPRIA), IEEE, 2019, pp. 240–245.

[8]     P. Viola and M. Jones, “Rapid object detection using a boosted cascade of simple features,” in Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001, Ieee, 2001, p. I–I. Accessed: Nov. 13, 2023. [Online]. Available: https://ieeexplore.ieee.org/abstract/document/990517/

[9]     W. A. Ezat, M. M. Dessouky, and N. A. Ismail, “Evaluation of deep learning yolov3 algorithm for object detection and classification,” Menoufia Journal of Electronic Engineering Research, vol. 30, no. 1, pp. 52–57, 2021.

[10]  K. Parviz, "Malware classification method using visualization features and word embedding based on deep learning," 2023, Accessed: Mar. 12, 2024. [Online]. Available: https://www.sid.ir/paper/1083015. (In Persian)

[11]  A. Zanganeh, M. Jampour, and K. Layeghi, “IAUFD: A 100k images dataset for automatic football image/video analysis,” IET Image Processing, vol. 16, no. 12, pp. 3133–3142, Oct. 2022, doi: 10.1049/ipr2.12543.

[12]  R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Region-based convolutional networks for accurate object detection and segmentation,” IEEE transactions on pattern analysis and machine intelligence, vol. 38, no. 1, pp. 142–158, 2015.

[13]  S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” Advances in neural information processing systems, vol. 28, 2015.

[14]  C.-Y. Fu, W. Liu, A. Ranga, A. Tyagi, and A. C. Berg, “DSSD : Deconvolutional Single Shot Detector.” arXiv, Jan. 23, 2017. Accessed: Nov. 13, 2023. [Online]. Available: http://arxiv.org/abs/1701.06659

[15]  Z. Shen, Z. Liu, J. Li, Y.-G. Jiang, Y. Chen, and X. Xue, “Dsod: Learning deeply supervised object detectors from scratch,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 1919–1927. Accessed: Nov. 13, 2023. [Online]. Available: http://openaccess.thecvf.com/content_iccv_2017/html/Shen_DSOD_Learning_Deeply_ICCV_2017_paper.html

[16]  G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700–4708.

[17]  [17] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779–788.

[18]  [18] J. Redmon and A. Farhadi, “YOLOv3: An Incremental Improvement.” arXiv, Apr. 08, 2018. Accessed: Nov. 13, 2023. [Online]. Available: http://arxiv.org/abs/1804.02767

[19]  [19] J. Redmon and A. Farhadi, “YOLO9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263–7271.

[20]  [20] T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2980–2988. Accessed: Dec. 13, 2023. [Online]. Available: http://openaccess.thecvf.com/content_iccv_2017/html/Lin_Focal_Loss_for_ICCV_2017_paper.html

[21]  W. Liu et al., “SSD: Single Shot MultiBox Detector,” in Computer Vision – ECCV 2016, vol. 9905, B. Leibe, J. Matas, N. Sebe, and M. Welling, Eds., in Lecture Notes in Computer Science, vol. 9905. , Cham: Springer International Publishing, 2016, pp. 21–37. doi: 10.1007/978-3-319-46448-0_2.