[1] Ted RMiller and Eduard Zaloshnja. Cost of Crashes Related to Road Conditions,
United States, 2006. Annals of Advances in Automotive, 53:141-53, 2019.
[2] Xingkui Zhu, et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the
IEEE/CVF international conference on computer vision, 2021, pp. 2778-2788.
[3] Deeksha Arya, et al. RDD2020: An annotated image dataset for automatic road
damage detection using deep learning. Data in brief, 2021, 36: 107133.
[4] Deeksha Arya, et al. RDD2022: A multi-national image dataset for automatic Road
Damage Detection. arXiv preprint arXiv:2209.08538, 2022.
[5] Online courses Stanford. CS231n Convolutional Neural Networks for Visual Recognition. Internet: http://cs231n.stanford.edu/.
[6] Global Road Damage Detection Challenge 2020. Internet: https://rdd2020.
sekilab.global/, 2020.
[7] Jacob Solawetz. YOLOv5 New Version - Improvements And Evaluation.
Internet: https://blog.roboflow.com/yolov5-improveme...,
June. 2020.
[8] Shu Liu, et al. Path aggregation network for instance segmentation. In: Proceedings of
the IEEE conference on computer vision and pattern recognition, 2018, pp. 8759-8768.
[9] Zhanchao Huang, et al. DC-SPP-YOLO: Dense connection and spatial pyramid pooling based YOLO for object detection. Information Sciences, 2020, 522: 241-258.
[10] Chien-Yao Wang, et al. CSPNet: A new backbone that can enhance learning capability of CNN. In: Proceedings of the IEEE/CVF conference on computer vision and
pattern recognition workshops, 2020, pp. 390-391.
[11] Gao Huang, et al. Densely connected convolutional networks. In: Proceedings of the
IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.
[12] Alexey Bochkovskiy, et al. Yolov4: Optimal speed and accuracy of object detection.
arXiv preprint arXiv:2004.10934, 2020.
[13] Kathuria and A.ken. What’s new in YOLO v3? Medium (2018). Internet: https:
//towardsdatascience.com/yolo-v3-object-detection-53fb7d3bfe6b.
[14] Yanjia LiYanjia Li. Dive Really Deep into YOLO v3. A Beginner’s Guide. Internet: https://towardsdatascience.com/
dive-really-deep-into-yolo-v3-a-beginnersguide-9e3d2666280e.
[15] Joseph Redmon and Ali Farhadi. Yolov3: An incremental improvement. arXiv
preprint arXiv:1804.02767, 2018.
[16] Jacob Solawetz. What are Anchor Boxes in Object Detection? Internet: https://
blog.roboflow.com/what-is-an-anchor-box/, June. 2020.
[17] Joseph Redmon and Ali Farhadi. YOLO9000: better, faster, stronger. In: Proceedings
of the IEEE conference on computer vision and pattern recognition, 2017, pp. 7263-
7271.
[18] Navaneeth Bodla, et al. Soft-NMS–improving object detection with one line of code.
In: Proceedings of the IEEE international conference on computer vision, 2017, pp.
5561-5569.
[19] CNN Bounding Box Predictions. Internet: https://datahacker.rs/
deep-learning-bounding-boxes/, Nov. 2018.
[20] Joseph Redmon , et al. 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.
[21] Mauriciomenegaz. Understanding YOLO hackernoon. Internet: https:
//hackernoon.com/understanding-yolo-f5a74bbc7967, March. 2018.
[22] Convolutional neural network for image classification with keras. Internet:
https://www.kernix.com/blog/a-toy-convolut...\
image-classification-with-keras_p14.
[23] Huu Vu Tiep. Machine learning cơ bản, 2017, pp.200–203. Internet: github.com/
tiepvupsu/ebookMLCB/blob/master/book_ML.pdf.2017.
[24] Hiroya Maeda, et al. Road damage detection using deep neural networks with images
captured through a smartphone. arXiv preprint arXiv:1801.09454, 2018.
[25] Brandon Rohrer. How convolutional neural networks work. Internet: http://
brohrer.github.io/how_convolutional_neural_networks_work.html.
[26] Chi Zhang, et al. Efficient eye typing with 9-direction gaze estimation. Multimedia
Tools and Applications, 2018, 77: 19679-19696.
[27] Xucong Zhang, et al. Appearance-based gaze estimation in the wild. In: Proceedings
of the IEEE conference on computer vision and pattern recognition, 2015, pp. 4511-
4520.
[28] Convolution operations. Internet: https://developer.apple.com/
library/content/documentation/Performance/Conceptual/vImage/
ConvolutionOperations/ConvolutionOperations.html.
[29] Y. Lecun, et al. Gradient-based learning applied to document recognition. Proceedings
of the IEEE, vol. 88, no. 11, pp. 2278 – 2324, Nov. 1998.
[30] Denny Britz. Neural networks for nlp. Internet: http://www.wildml.com/2015/11/
understanding\-convolutional-neural-networks-for-nlp/.
[31] Trần Thế Anh. Convolution neural network. Internet: http://labs.septeni-technology.jp/technote/
ml-20-convolution-neural-network-part-3/.
.................
...................
Информация по комментариям в разработке