The Analysis of Underwater Imagery System for Armor Unit Monitoring Application

Dewi Mutiara Sari; Bayu Sandi  Marta; Muhammad  Amin A; Haryo  Dwito Armono

doi:10.25139/ijair.v5i1.5918

Dewi Mutiara Sari Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
Bayu Sandi Marta Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
Muhammad Amin A Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya
Haryo Dwito Armono Ocean Engineering Department, Faculty of Marine Engineering, Institut Teknologi Sepuluh Nopember

https://doi.org/10.25139/ijair.v5i1.5918

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PDF downloads: 168

Keywords: Armor Unit, Underwater Color Image Quality Evaluation, Underwater Image Quality Measures, Image Enhancement, Image Color Restoration, Frame per Second

Abstract

The placement of armor units for breakwaters in Indonesia is still done manually, which depends on divers in each placement of the armor unit. The use of divers is less effective due to limited communication between divers and excavator operators, making divers in the water take a long time. This makes the diver's job risky and expensive. This research presents a vision system to reduce the diver's role in adjusting the position of each armor unit. This vision system is built with two cameras connected to a mini-computer. This system has an image improvement process by comparing three methods. The results obtained are an average frame per second is 20.71 without applying the method, 0.45 fps for using the multi-scale retinex with color restoration method, 16.75 fps for applying the Contrast Limited Adaptive Histogram Equalization method, 16.17 fps for applying the Histogram Equalization method. The image quality evaluation uses the underwater color quality evaluation with 48 data points. The method that has experienced the most improvement in image quality is multi-scale retinex with color restoration. Forty data have improved image quality with an average of 14,131, or 83.33%. The number of images that experienced the highest image quality improvement was using the multi-scale retinex with color restoration method. Meanwhile, for image quality analysis based on Underwater Image Quality Measures, out of a total of 48 images, the method with the highest value for image quality is the contrast limited adaptive histogram equalization method. 100% of images have the highest image matrix value with an average value is 33.014.

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Author Biographies

Dewi Mutiara Sari, Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya

Bayu Sandi Marta, Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya

Muhammad Amin A, Departement of Informatics and Computer Engineering, Politeknik Elektronika Negeri Surabaya

Haryo Dwito Armono, Ocean Engineering Department, Faculty of Marine Engineering, Institut Teknologi Sepuluh Nopember

References

L. R. Saragih, M. Dachyar, T. Y. M. Zagloel and M. Satar, "The Industrial IoT for Nusantara," 2018 IEEE International Conference on Internet of Things and Intelligence System (IOTAIS), 2018, pp. 73-79, doi: 10.1109/IOTAIS.2018.8600860.

Dupray, Sébastien & Roberts, Jim. (2010). Review of the use of concrete in the manufacture of concrete armour units. Coasts, Marine Structures and Breakwaters: Adapting to Change - Proceedings of the 9th International Conference. 1. 245-259. 10.1680/cmsb.41301.0021.

Peter Van Gelderen and Stephen Auld, "Innovative technique for single layer armour unit placement", Coasts, marine structures and breakwaters: Adapting to change,pp. 425-436, doi:10.1680/cmsb.41301.0037.

Rover, Richard & Reedijk, Bas & Bakker, Pieter. (2014). “Xbloc Innovations At Swinoujscie Breakwater”. Coastal Engineering Proceedings. 1. 62. 10.9753/icce.v34.structures.62.

Y. Wang, W. Song, G. Fortino, L. Qi, W. Zhang and A. Liotta, "An Experimental-Based Review of Image Enhancement and Image Restoration Methods for Underwater Imaging," in IEEE Access, vol. 7, pp. 140233-140251, 2019, doi: 10.1109/ACCESS.2019.2932130.

Sergei Yelmanov, Olena Hranovska and Yuriy Romanyshyn, "A New Approach to the Implementation of Histogram Equalization in Image Processing" 2019 3rd International Conference on Advanced Information and Communications Technologies (AICT), 2019 , Lviv, Ukraine, doi: 10.1109/AIACT.2019.8847920

Cosmin Ancuti, Codruta Orniana Ancuti, Tom Hober and Philipppe Bekaert, "Enhancing Underwater Images and Videos by Fusion", IEEE Conference on Computer Vision and Pattern Recognition, 10.1109/CVPR.2012.6247661

Bayu Sandi Marta, Muhammad Amin A, Dewi Mutiara Sari, Haryo Dwito Armono. "The Comparison of Image Enhancement Methods for A Real-time Under Water Vision System of The Break Water Implementation", 2022 International Electronics Symposium (IES), 2022

Lisani, Jose-Luis, et al, "Analyzing Center/Surround Retinex," ELSEIVER, 2020.

Zakaria, Andi Irvan,et al, "Perbandingan Metode High-High Frequency Emphasis (HFE) Dan Contrast Limited Adaptive Histogram Equalization (CLAHE) Dalam Perbaikan Kualitas Citra Penginderaan Jauh (Remote Sensing)," in Jurnal Pseucode, Vol.4 No.2 2019.

Ce Gao, Lijun Yun, Kun wang, Zhixia Ye, and Huijun Li, "Infrared Image Enhancement Method Based on Discrete stationary Wavelet Transform and CLAHE," IEEE International Conference on Computer Science and Educational Informatization (CSEI), pp. 191-194, August 2019.

Kwok, Ngaimin, et al, "Single-Scale Center-Surround Retinex Based Restoration ofLow-illumination Images with Edge Enhancement," Ninth International Conference on Graphic and Image Processing, 2017.

Zhang, Shu; Wang, Ting; Dong, Junyu; Yu, Hui (2017). Underwater image enhancement via extended multi scale Retinex. Neurocomputing, 245(), 1–9. doi:10.1016/j.neucom.2017.03.029

S. Parthasarathy and P. Sankaran, "An automated multi Scale Retinex with Color Restoration for image enhancement," 2012 National Conference on Communications (NCC), 2012, pp. 1-5, doi: 10.1109/NCC.2012.6176791.

M. Yang and A. Sowmya, "An Underwater Color Image Quality Evaluation Metric," in IEEE Transactions on Image Processing, vol. 24, no. 12, pp. 6062-6071, Dec. 2015, doi: 10.1109/TIP.2015.2491020.

K. Panetta, C. Gao and S. Agaian, "Human-Visual-System-Inspired Underwater Image Quality Measures," in IEEE Journal of Oceanic Engineering, vol. 41, no. 3, pp. 541-551, July 2016, doi: 10.1109/JOE.2015.2469915.

Raspberry Pi, "The official website of the Raspberry Pi project," http://raspberry.org. Accessed 22 June, 2021.

Zhu Youlian, et al, "An Adaptive Histogram Equalization Algorithm on the Image Gray Level Mapping", International Conference on Solid State Device and Materials Science, 2012.