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Microalgae Detection Using a Deep Learning Object Detection Algorithm, YOLOv3

딥러닝 사물 인식 알고리즘(YOLOv3)을 이용한 미세조류 인식 연구

  • Received : 2021.05.11
  • Accepted : 2021.07.14
  • Published : 2021.07.30

Abstract

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

Keywords

Acknowledgement

This work was supported by Korea Environment Industry &Technology Institute (KEITI) through Aquatic Ecosystem Conservation Research Program, funded by Korea Ministry of Environment (MOE) (2020003030006) and the Nakdonggang National Institute of Biological Resources (NNIBR), funded by the Ministry of Environment (MOE) of the Republic of Korea (NNIBR202101103).

References

  1. Codd, G. A., Morrison, L. F., and Metcalf, J. S. (2005). Cyanobacterial toxins: risk management for health protection, Toxicology and Applied Pharmacology, 203, 264-272. https://doi.org/10.1016/j.taap.2004.02.016
  2. Girshick, R. (2015). Fast r-cnn, Proceedings of the IEEE International Conference on Computer Vision, 1440-1448.
  3. Girshick, R., Donahue, J., Darrell, T., and Malik, J. (2014). Rich feature hierarchies for accurate object detection and semantic segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 580-587.
  4. He, K., Gkioxari, G., Dollar, P., and Girshick, R. (2017). Mask r-cnn, Proceedings of the IEEE International Conference on Computer Vision, 2961-2969.
  5. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916. https://doi.org/10.1109/TPAMI.2015.2389824
  6. Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, 25, 1097-1105.
  7. LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning, Nature, 521, 436-444. https://doi.org/10.1038/nature14539
  8. Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollar, P. (2017). Focal loss for dense object detection, Proceedings of the IEEE International Conference on Computer Vision, 2980-2988.
  9. Liu, W. Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., and Berg, A. C. (2016). SSD: Single shot multibox detector, Proceedings of European Conference on Computer Vision, 21-37.
  10. Ozenne, B., Subtil, F., and Maucort-Boulch, D. (2015). The precision-recall curve overcame the optimism of the receiver operating characteristic curve in rare diseases, Journal of Clinical Epidemiology, 68, 855-859. https://doi.org/10.1016/j.jclinepi.2015.02.010
  11. Paerl, H. W. and Otten, T. G. (2013). Harmful cyanobacterial blooms: causes, consequences, and controls, Microbial Ecology, 65, 995-1010. https://doi.org/10.1007/s00248-012-0159-y
  12. Pedraza, A., Bueno, G., Deniz, O., Ruiz-Santaquiteria, J., Sanchez, C., Blanco, S., Borrego-Ramos, M., Olenici, A., and Cristobal, G. (2018). Lights and pitfalls of convolutional neural networks for diatom identification, Proceedings of Optics, Photonics, and Digital Technologies for Imaging Applications V, 106790G.
  13. Redmon, J. and Farhadi, A. (2017). YOLO9000: Better, faster, stronger, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 7263-7271.
  14. Redmon, J. and Farhadi, A. (2018). Yolov3: An incremental improvement, arXiv preprint arXiv, 1804.02767.
  15. Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You only look once: Unified, real-time object detection, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 779-788.
  16. Ren, S., He, K., Girshick, R., and Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks, arXiv preprint arXiv, 1506.01497.
  17. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., and Bernstein, M. (2015). Imagenet large scale visual recognition challenge, International Journal of Computer Vision, 115, 211-252. https://doi.org/10.1007/s11263-015-0816-y
  18. Salido, J., Sanchez, C., Ruiz-Santaquiteria, J., Cristobal, G., Blanco, S., and Bueno, G. (2020). A low-cost automated digital microscopy platform for automatic identification of diatoms, Applied Sciences, 10, 6033. https://doi.org/10.3390/app10176033
  19. Sultana, F., Sufian, A., and Dutta, P. (2020). A review of object detection models based on convolutional neural network, Intelligent Computing: Image Processing Based Applications, 1-16.
  20. Tian, Y., Yang, G., Wang, Z., Wang, H., Li, E., and Liang, Z. (2019). Apple detection during different growth stages in orchards using the improved YOLO-V3 model, Computers and Electronics in Agriculture, 157, 417-426. https://doi.org/10.1016/j.compag.2019.01.012
  21. World Health Organization (WHO). (2004). Guidelines for drinking-water quality, Geneva, Switzland, Volume 1, World Health Organization.
  22. Zhao, K. and Ren, X. (2019). Small aircraft detection in remote sensing images based on YOLOv3, Proceedings of IOP Conference Series: Materials Science and Engineering, 012056.
  23. Zhao, Z. Q., Zheng, P., Xu, S. T., and Wu, X. (2019). Object detection with deep learning: A review, IEEE Transactions on Neural Networks and Learning Systems, 30(11), 3212-3232. https://doi.org/10.1109/tnnls.2018.2876865