The Journal of
the Korean Society on Water Environment

The Journal of
the Korean Society on Water Environment

Bimonthly
  • ISSN : 2289-0971 (Print)
  • ISSN : 2289-098X (Online)
  • KCI Accredited Journal

Editorial Office

  1. Home
  2. All Issues
  3. 2021-07 (Vol. 37, No. 4)
  4. 10.15681/KSWE.2021.37.4.275
XML PDF INFO REF

References

1 
Codd G. A., Morrison L. F., Metcalf J. S., 2005, Cyanobacterial toxins: risk management for health protection, Toxicology and Applied Pharmacology, Vol. 203, pp. 264-272DOI
2 
Girshick R., 2015, Fast r-cnn, Proceedings of the IEEE International Conference on Computer Vision, pp. 1440-1448DOI
3 
Girshick R., Donahue J., Darrell T., Malik J., 2014, Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 580-587DOI
4 
He K., Gkioxari G., Dollár P., Girshick R., 2017, Mask r-cnn, Proceedings of the IEEE International Conference on Computer Vision, pp. 2961-2969DOI
5 
He K., Zhang X., Ren S., Sun J., 2015, Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 37, pp. 1904-1916DOI
6 
Krizhevsky A., Sutskever I., Hinton G. E., 2012, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, Vol. 25, pp. 1097-1105Google Search
7 
LeCun Y., Bengio Y., Hinton G., 2015, Deep learning, Nature, Vol. 521, pp. 436-444DOI
8 
Lin T. Y., Goyal P., Girshick R., He K., Dollár P., 2017, Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, pp. 2980-2988DOI
9 
Liu W., Anguelov D., Erhan D., Szegedy C., Reed S., Fu C. Y., Berg A. C., 2016, SSD: Single shot multibox detector, Proceedings of European Conference on Computer Vision, pp. 21-37DOI
10 
Ozenne B., Subtil F., Maucort-Boulch D., 2015, The precision–recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases, Journal of Clinical Epidemiology, Vol. 68, pp. 855-859DOI
11 
Paerl H. W., Otten T. G., 2013, Harmful cyanobacterial blooms: causes, consequences, and controls, Microbial Ecology, Vol. 65, pp. 995-1010DOI
12 
Pedraza A., Bueno G., Deniz O., Ruiz-Santaquiteria J., Sanchez C., Blanco S., Borrego-Ramos M., Olenici A., Cristobal G., 2018, Lights and pitfalls of convolutional neural networks for diatom identification, Proceedings of Optics, Photonics, and Digital Technologies for Imaging Applications V, 106790GDOI
13 
Redmon J., Farhadi A., 2017, YOLO9000: Better, faster, stronger, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263-7271DOI
14 
Redmon J., Farhadi A., 2018, Yolov3: An incremental improvement, arXiv preprint arXiv, 1804.02767Google Search
15 
Redmon J., Divvala S., Girshick R., Farhadi A., 2016, You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788DOI
16 
Ren S., He K., Girshick R., Sun J., 2015, Faster r-cnn: Towards real-time object detection with region proposal networks, arXiv preprint arXiv, 1506.01497Google Search
17 
Russakovsky O., Deng J., Su H., Krause J., Satheesh S., Ma S., Huang Z., Karpathy A., Khosla A., Bernstein M., 2015, Imagenet large scale visual recognition challenge, International Journal of Computer Vision, Vol. 115, pp. 211-252DOI
18 
Salido J., Sánchez C., Ruiz-Santaquiteria J., Cristóbal G., Blanco S., Bueno G., 2020, A low-cost automated digital microscopy platform for automatic identification of diatoms, Applied Sciences, Vol. 10, pp. 6033DOI
19 
Sultana F., Sufian A., Dutta P., 2020, A review of object detection models based on convolutional neural network, Intelligent Computing: Image Processing Based Applications, pp. 1-16DOI
20 
Tian Y., Yang G., Wang Z., Wang H., Li E., Liang Z., 2019, Apple detection during different growth stages in orchards using the improved YOLO-V3 model, Computers and Electronics in Agriculture, Vol. 157, pp. 417-426DOI
21 
World Health Organization (WHO), 2004, Guidelines for drinking-water quality, World Health Organization, Vol. 1
22 
Zhao K., Ren X., 2019, Small aircraft detection in remote sensing images based on YOLOv3, Proceedings of IOP Conference Series: Materials Science and Engineering, 012056DOI
23 
Zhao Z. Q., Zheng P., Xu S. T., Wu X., 2019, Object detection with deep learning: A review, IEEE Transactions on Neural Networks and Learning Systems, Vol. 30, No. 11, pp. 3212-3232DOI
뒤로가기