TECHNOLOGICAL BASIS OF “INDUSTRY 4.0”
Deep Eye – Intelligent Device for Creating Digital Models of Underwater Topography
- 1 AGH university of Krakow, Poland
Abstract
This project endeavors to advance underwater mapping technologies through the development of algorithms tailored for mapping, analyzing, and creating digital models of bathymetry. A key aspect of this research involves the integration of advanced technologies, specifically High-Definition cameras and a purpose-designed sonar system. By leveraging these tools, high-resolution point cloud data and precise representations of submerged geographical structures can be obtained. The project also aims to conduct a waterproof device, operated by a diver, incorporating both HD cameras and sonar sensors. This device will enhance data collection and mapping capabilities in challenging underwater environments, addressing complexities associated with underwater terrains. Several critical questions are central to this research. Primarily, the investigation seeks to understand how artificial intelligence algorithms can be optimized to harness the collective potential of HD cameras and the specially crafted sonar system. Additionally, the research endeavors to evaluate the comparative efficacy and accuracy of the proposed sonar system and HD cameras in underwater mapping, particularly under adverse environmental conditions where visibility is compromised by high particle concentrations. Througth these efforts, this project aims to contribute to the advancement of underwater mapping technologies, offering insights into optimizing sensor integration and algorithm development for enhanced mapping accuracy in challenging underwater environments.
Keywords
References
- Alpers, W., & Hennings, I. (1984). A theory of the imaging mechanism of underwater bottom topography by real and synthetic aperture radar. Journal of Geophysical Research: Oceans, 89(C6), 10529-10546.
- Leng, Z., Zhang, J., Ma, Y., & Zhang, J. (2020). Underwater topography inversion in liaodong shoal based on GRU deep learning model. Remote Sensing, 12(24), 4068.
- Iwakami, S., Tamega, M., Sanada, M., Mohri, M., Iwakami, Y., Okamoto, N., & Watanabe, M. (2021). Mathematical modeling and computational analysis of underwater topography with global positioning and echo sounder data. Journal of Applied Mathematics and Physics, 9(5), 1171-1179.
- Zhang, W., Zhuang, P., Sun, H. H., Li, G., Kwong, S., & Li, C. (2022). Underwater image enhancement via minimal color loss and locally adaptive contrast enhancement. IEEE Transactions on Image Processing, 31, 3997-4010.
- Zhuang, P., Li, C., & Wu, J. (2021). Bayesian retinex underwater image enhancement. Engineering Applications of Artificial Intelligence, 101, 104171.
- Zhou, J., Sun, J., Zhang, W., & Lin, Z. (2023). Multi-view underwater image enhancement method via embedded fusion mechanism. Engineering Applications of Artificial Intelligence, 121, 105946.
- Lin, H., Zhang, H., Li, Y., Wang, H., Li, J., & Wang, S. (2020). 3D point cloud capture method for underwater structures in turbid environment. Measurement Science and Technology, 32(2), 025106.
- Tsai, C. M., Lai, Y. H., Sun, Y. D., Chung, Y. J., & Shortis, M. (2021). Multi-dimensional underwater point cloud detection based on deep learning. Sensors, 21(3), 884.
- Wang, X. X., Gao, J., & Feng, L. (2020, August). Recognition and 3D pose estimation for underwater objects using deep convolutional neural network and point cloud registration. In 2020 International Conference on System Science and Engineering (ICSSE) (pp. 1-6). IEEE.
- Hidalgo, F. (2020, October). ORBSLAM2 and point cloud processing towards autonomous underwater robot navigation. In Global Oceans 2020: Singapore–US Gulf Coast (pp. 1-4). IEEE.Balestrieri, E., Daponte, P., de Vito, L., & Lamonaca, F. (2021). Sensors and Measurements for Unmanned Systems: An Overview. Sensors, 21(4). https://doi.org/10.3390/s21041518
- Muja, M., & Lowe, D. (2009). Flann-fast library for approximate nearest neighbors user manual. Computer Science Department, University of British Columbia, Vancouver, BC, Canada, 5, 6.