• Tìm hiểu các phương pháp tăng cường bộ dữ liệu.
• Tìm hiểu về các kỹ thuật sử dụng trong detector để cải thiện mô hình về độ chính xác và thời gian tính toán.
74
TÀI LIỆU THAM KHẢO
[1] Ren, S., He, K., Girshick, R., & Sun, J. (2016). Faster R-CNN: towards real- time object detection with region proposal networks. IEEE transactions on pattern analysis and machine intelligence, 39(6), 1137-1149.
[2] Cao, J., Cholakkal, H., Anwer, R. M., Khan, F. S., Pang, Y., & Shao, L. (2020). D2det: Towards high quality object detection and instance segmentation. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 11485-11494).
[3] Qiao, S., Chen, L. C., & Yuille, A. (2021). Detectors: Detecting objects with recursive feature pyramid and switchable atrous convolution. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10213-10224).
[4] Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021, October). Tood: Task-aligned one-stage object detection. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (pp. 3490-3499). IEEE Computer Society.
[5] Zhang, H., Wang, Y., Dayoub, F., & Sunderhauf, N. (2021). Varifocalnet: An iou-aware dense object detector. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 8514-8523).
[6] Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., ... & Tian, Q. (2018). The unmanned aerial vehicle benchmark: Object detection and tracking. In Proceedings of the European conference on computer vision (ECCV) (pp. 370- 386).
[7] Razakarivony, S., & Jurie, F. (2016). Vehicle detection in aerial imagery: A small target detection benchmark. Journal of Visual Communication and Image
75 Representation, 34, 187-203.
[8] Zhu, P., Wen, L., Du, D., Bian, X., Hu, Q., & Ling, H. (2020). Vision meets drones: Past, present and future. arXiv preprint arXiv:2001.06303.
[9] Ding, J., Zhu, Z., Xia, G. S., Bai, X., Belongie, S., Luo, J., ... & Zhang, L. (2018, August). Icpr2018 contest on object detection in aerial images (odai- 18). In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 1-6). IEEE.
[10] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).
[11] Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 580-587).
[12] Girshick, R. (2015). Fast r-cnn. In Proceedings of the IEEE international conference on computer vision (pp. 1440-1448).
[13] Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., & Belongie, S. (2017). Feature pyramid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2117-2125).
[14] Dai, J., Qi, H., Xiong, Y., Li, Y., Zhang, G., Hu, H., & Wei, Y. (2017). Deformable convolutional networks. In Proceedings of the IEEE international conference on computer vision (pp. 764-773).
[15] Cai, Z., & Vasconcelos, N. (2018). Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6154-6162).
76
[16] Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision (pp. 2980-2988).
77
PHỤ LỤC A – BÀI BÁO
Bài báo khoa học “Phát hiện phương tiện giao thông trong không ảnh với nhiều tình huống khác nhau” được đăng tại Hội thảo Quốc gia lần thứ XXIV về Điện