Zimo Zhou


  1. [1] J. Rutherford and J.M. Webster, Distribution of pine wilt disease with respect to temperate in North America, Japan, and Europe, Canadian Journal of Forest Research, 17, 1987, 1050–1059.
  2. [2] S. Ren, K. He, R. Girshick, and J. Sun, Faster R-CNN: Towards real-time object detection with region proposal networks, NIPS, 2015, 91–99.
  3. [3] S. Rathinam, P. Almeida, Z. Kim; S. Jackson, A. Tinka, W. Grossman, and R. Sengupta, Autonomous searching and tracking of a river using an UAV, 2007 American Control Conference, New York, NY, 2007, 359–364.
  4. [4] B. Benjdira, T. Khursheed, A. Koubaa, A. Ammar, and K. Ouni, Car detection using unmanned aerial vehicles: Comparison between faster R-CNN and YOLOv3, 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman, 2019, pp. 1–6.
  5. [5] L. Jangwon, W. Jingya, C. David, S. Selma, and F. Geoffrey, IEEE 2017 First IEEE International Conference on Robotic Computing (IRC) – Taichung, Taiwan, 2017, 2017.4.10– 2017.4.12.
  6. [6] C. Ovidiu, C. John, J. Robert, L. Andy, and K. Maggi, Identification of Citrus Trees from Unmanned Aerial Vehicle Imagery Using Convolutional Neural Networks Drones 2, 4, 2018, 39.
  7. [7] P. Lottes, R. Khanna, J. Pfeifer, R. Siegwart, and C. Stachniss, UAV-based crop and weed classification for smart farming, 2017 IEEE International Conference on Robotics and Automation (ICRA), Singapore, 2017, 3024–3031.
  8. [8] C. Lisa, M. Corniglia, M. Gaetani, N. Grossi, S. Magni, M. Migliazzi, and L. Angelini, Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PloS One, 11(6), 2016; 6(2), 2017, e00415.
  9. [9] R. Girshick, J. Donahue, T. Darrell, and J. Malik, Rich feature hierarchies for accurate object detection and semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014.
  10. [10] R. Girshick, Fast R-CNN, Proceedings of the 2015 IEEE International Conference on Computer Vision (ICCV) (ICCV ’15) (USA: IEEE Computer Society, 2015), 1440–1448.
  11. [11] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, You only look once: unified, real-time object detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, 779–788.
  12. [12] C. Binghuang and X. Miao, Distribution line pole detection and counting based on YOLO using UAV inspection line video, Journal of Electrical Engineering & Technology, 15, 2020, 441–448.
  13. [13] H. Chen, Z. He, B. Shi, and T. Zhong, Research on recognition method of electrical components based on YOLO V3, IEEE Access, 7, 2019, 157818–157829.
  14. [14] Y. Xu, G. Yu, Y. Wang, X. Wu, and Y. Ma, Car detection from low-altitude UAV imagery with the faster R-CNN, Journal of Advanced Transportation, 2017, Article ID 2823617, 2017, 10 pages.
  15. [15] “Bursaphelenchus xylophilus”. Wikipedia.
  16. [16] T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollr, and C.L. Zitnick, Microsoft COCO: Common objects in context, in D. Fleet, T. Pajdla, B. Schiele, and T. Tuytelaars (eds.), Computer Vision – ECCV 2014. ECCV 2014. Lecture Notes in Computer Science, vol. 8693 (Springer, Cham, 2014).
  17. [17] J. Pang, K. Chen, J. Shi, H. Feng, W. Ouyang, and D. Lin, Libra R-CNN: Towards balanced learning for object detection, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, 821–830.
  18. [18] J. Wang, K. Chen, S. Yang, C.C. Loy, and D. Lin, Region proposal by guided anchoring, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, USA, 2019, 2960–2969.
  19. [19] J. Redmon and A. Farhadi, Yolov3: An incremental improvement, arXiv preprint arXiv:1804.02767, 2018.
  20. [20] G. Jocher,
  21. [21] A. Bochkovskiy, C.Y. Wang, and H.M. Liao, YOLOv4: Optimal speed and accuracy of object detection. ArXiv, abs/2004.10934, 2020.
  22. [22] D. Tzutalin,
  23. [23] T.Y. Lin, P. Dollar, R. Girshick, K. He, B. Hariharan, and S. Belongie, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 2117–2125.
  24. [24] Z. Zheng, P. Wang, W. Liu, J. Li., R. Ye, and D. Ren, 2020. Distance-IoU loss: Faster and better learning for bounding box regression, Proceedings of the AAAI Conference on Artificial Intelligence, 34, 07 April 2020, 12993–13000.
  25. [25] L. Tsung-Yi, P. Dollár, R. Girshick, K. He, B. Hariharan, and S.J. Belongie, Feature pyramid networks for object detection. 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, 936–944.

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