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Marine Organisms Tracking and Recognizing Using YOLO

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Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

A system that investigates deep sea automatically has never developed. A purpose of this study is developing such a system. We employed a technique of recognition and tracking of multi-objects, called “You Only Look Once: YOLO.” This method provides us very fast and accurate tracker. In our system, we remove the haze, which is caused by turbidity of water, from image. After its process, we apply “YOLO” to tracking and recognizing the marine organisms, which includes shrimp, squid, crab, and shark. Our developed system shows generally satisfactory performance.

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References

  1. Redmon, J., Divvala, S. K., Girshick, R. B., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 779–788).

    Google Scholar 

  2. Redmon, J., & Farhadi, A. (2017). YOLO9000: Better, faster, stronger. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6517–6525).

    Google Scholar 

  3. Li, Y., Lu, H., Li, J., Li, X., Li, Y., & Serikawa, S. (2016). Underwater image de-scattering and classification by deep neural network. Computers and Electrical Engineering, 54, 68–77.

    Article  Google Scholar 

  4. Lu, H., Li, Y., Uemura, T., Ge, Z., Xu, X., He, L., Serikawa, S., & Kim, H. (2017). FDCNet: Filtering deep convolutional network for marine organism classification. Multimedia Tools and Applications, 77, 21847–21860.

    Article  Google Scholar 

  5. Lu, H., Li, Y., Mu, S., Wang, D., Kim, H., & Serikawa, S. (2017). Motor anomaly detection for unmanned aerial vehicles using reinforcement learning. IEEE Internet of Things, 5(4), 2315–2322.

    Article  Google Scholar 

  6. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2018). Brain intelligence: Go beyond artificial intelligence. Mobile Networks and Application, 23(2), 368–375.

    Article  Google Scholar 

  7. Lu, H., Li, Y., Nakashima, S., & Serikawa, S. (2016). Turbidity underwater image restoration using spectral properties and light compensation. IEICE Transactions on Information and Systems, E-99D(1), 219–226.

    Article  Google Scholar 

  8. Lu, H., Li, Y., Zhang, L., & Serikawa, S. (2015). Contrast enhancement for images in turbid water. Journal of Optical Society of America A, 32(5), 886–893.

    Article  Google Scholar 

  9. Serikawa, S., & Lu, H. (2014). Underwater image dehazing using joint trilateral filter. Computers and Electrical Engineering, 40(1), 41–50.

    Article  Google Scholar 

  10. Chen, M., Yang, J., Hao, Y., Mao, S., & Hwang, K. (2017). A 5G cognitive system for healthcare. Big Data and Cognitive Computing, 1(1), 2. https://doi.org/10.3390/bdcc1010002.

    Article  Google Scholar 

  11. Chen, M., Shi, X., Zhang, Y., Wu, D., & Guizani, M. (2017). Deep features learning for medical image analysis with convolutional autoencoder neural network. IEEE Transactions on Big Data. https://doi.org/10.1109/TBDATA.2017.2717439.

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Acknowledgments

This work was supported by JSPS KAKENHI (17K14694), Leading Initiative for Excellent Young Researcher (LEADER) of Ministry of Education, Culture, Sports, Science, and Technology—Japan (16809746), Research Fund of State Key Laboratory of Marine Geology in Tongji University (MGK1803), Research Fund of State Key Laboratory of Ocean Engineering in Shanghai Jiaotong University (1510), Research Fund of The Telecommunications Advancement Foundation, Fundamental Research Developing, Association for Shipbuilding and Offshore and Strengthening Research Support Project of Kyushu Institute of Technology. We also thank JAMSTEC for offering the datasets.

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Correspondence to Huimin Lu .

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Uemura, T., Lu, H., Kim, H. (2020). Marine Organisms Tracking and Recognizing Using YOLO. In: Lu, H., Yujie, L. (eds) 2nd EAI International Conference on Robotic Sensor Networks. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-17763-8_6

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  • DOI: https://doi.org/10.1007/978-3-030-17763-8_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-17762-1

  • Online ISBN: 978-3-030-17763-8

  • eBook Packages: EngineeringEngineering (R0)

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