Underwater Fleck Detection Using Convolutional Neural Network

被引：2

作者：

Pushpa Mala S. ^{[1
]}

Prajwal Raju P. ^{[1
]}

Poojashree B. ^{[1
]}

Hebbar R. ^{[1
]}

Bedre V. ^{[1
]}

Manasa K.R. ^{[1
]}

机构：

[1] Department of Electronics and Communication Engineering, Dayananda Sagr University, Kudlu Gate, Karnataka, Bengaluru

来源：

Journal of The Institution of Engineers (India): Series B | 2024年 / 105卷 / 02期

关键词：

CNN; Computer vision; Fleck; Underwater;

D O I：

10.1007/s40031-023-00949-1

中图分类号：

学科分类号：

摘要：

Monitoring of pollution in water through connected smart devices is impracticable, still being an unexplored and far-fetched domain to investigate. Qualitative identification is typically used to depict the visible or aesthetic characteristics of water. Although the underwater environment remains unexplored and cumbersome to investigate, due to divergent prodigious activities, an underwater fleck detection method is proposed espousing the common visual features of water to investigate the underwater particulate matter. A 3D model for the proposed navigation unit and an application is modelled to determine the concentration of the particulate matter in the hydro samples collected from the surface, the mid, bottom level of a stagnant water body. The ML model is trained, and the execution time, RAM utilization, memory utilization, overall accuracy, model accuracy, and loss are analysed for the proposed system. © The Institution of Engineers (India) 2023.

引用

页码：365 / 373

页数：8

共 15 条

[1]

Zhe C., Zhang Z., Dai F., Bu Y., Wang H., Monocular vision-based underwater object detection, Sensors, 17, 8, (2017)

[2]

Ankit G., Ruebush E., Aquasight: Automatic Water Impurity Detection Utilizing Convolutional Neural Networks. Arxiv Preprint Arxiv, 1907, (2019)

[3]

Wahid J., Ahsan Q., Detection of impurities in water by measuring capacitance, 8Th International Conference on Electrical and Computer Engineering, pp. 88-91, (2014)

[4]

Rizzini D.L., Kallasi F., Oleari F., Caselli S., Investigation of vision-based underwater object detection with multiple datasets, Int. J. Adv. Rob. Syst, 12, 6, (2015)

[5]

Image enhancement by curvelet, ridgelet, and wavelet transform, Proc. SPIE 7546, Second International Conference on Digital Image Processing, 754636, 26 February, (2010)

[6]

Nagtode S.A., Choudhari N.K., Detection of impurity in liquids using electronic sensor based system with infrared camera, Int. J. Innov. Res. Electr., Electron. Instrum. Control Eng, 4, pp. 17-21, (2012)

[7]

Nagtode Smita A., Choudhari N.K., Electronic sensor based system for detecting impurity level in liquids using additive wavelet transform, IEEE Fifth International Conference on Advances in Recent Technologies in Communication and Computing, pp. 255-260, (2013)

[8]

Huang H., Sun Z., Liu S., Di Y., Xu J., Liu C., Xu R., Song H., Zhan S., Wu J., Underwater hyperspectral imaging for in situ underwater microplastic detection, Sci. Total. Environ, 776, (2021)

[9]

Tabella G., Paltrinieri N., Cozzani V., Rossi P.S., Wireless sensor networks for detection and localization of subsea oil leakages, IEEE Sens. J, 21, 9, pp. 10890-10904, (2021)

[10]

Tabella G., Paltrinieri N., Cozzani V., Salvo Rossi P., Data fusion for subsea oil spill detection through wireless sensor networks, Proc. IEEE Sens, 82, pp. 1-4, (2020)

← 1 2 →