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Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data

DETEKTOVANJE PODVODNIH METALNIH OBJEKATA POMOĆU VEŠTAČKE INTELIGENCIJE I ZAŠTITA OD KOROZIJE PREDMETA OD MEKOG ČELIKA KORIŠĆENIH U PODVODNOJ STUDIJI-STUDIJA SLUČAJA Deo A-detektovanje podvodnih metalnih objekata pomoću veštačke inteligencije

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2022
Deep_learning_based_underwater_pub_2022.pdf (996.7Kb)
Authors
Rajendran, Dorothy
Sasilatha, Thankappan
Mary, Suvakeen Amala Doss Hebciba
Rajendran, Susai Santhammal
Lačnjevac, Časlav
Singh, Gurmeete
Article (Published version)
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Abstract
Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a pre-processing procedure, is necessary for underwater vision. In this paper, introduced the Deep learning based Underwater Metal object detection using input Image data by using several step to improve the model performance. In this experimentation we are using TURBID dataset 100 images to validate the performance. And also we compare the performance result by given the input images in different validation level. In first input image is initially preprocessed and that images is given to the KFCM-Segmentation. The segmented images are given to the DWT Extraction to extract the fe...atures from those images. And finally the Convolution Neural Network (CNN) is used to classify the images to detect the objects. Also this proposed model attained the classification accuracy of 98.83%. This method is much suitable for detect the objects in underwater robotically. Metallic parts of machines of ships or aero planes may submerge in sea water. They may undergo corrosion when they come in contact with sea water which contains 3.5% sodium chloride. This is most commonly responsible for the corrosive nature of the sea water. The robots made of materials such as mild steel may also undergo corrosion when they come in contact with sea water, while is search. If a paint coating is given, it will control the corrosion of these proposed materials. Hence this work is undertaken. Mild steel is coated with Asian guard red paint. Corrosion resistance of mild in3.5% sodium chloride solution is measured before coating and after coating by electrochemical studies such as such as polarization study and AC impedance spectra. The corrosion inhibition efficiency offered by red paint to mild steel in 3.5% sodium chloride is 99.98%.

Keywords:
AC impedance spectra / Convolutional Neural Network (CNN) / Fuzzy c-means clustering and TURBID dataset polarization study / input Image data / sea water
Source:
Materials Protection, 2022, 63, 1, 5-14
Publisher:
  • Engineers Society for Corrosion

DOI: 10.5937/zasmat2201005R

ISSN: 0351-9465

Scopus: 2-s2.0-85127200967
[ Google Scholar ]
URI
http://aspace.agrif.bg.ac.rs/handle/123456789/6059
Collections
  • Radovi istraživača / Researchers’ publications
Institution/Community
Poljoprivredni fakultet
TY  - JOUR
AU  - Rajendran, Dorothy
AU  - Sasilatha, Thankappan
AU  - Mary, Suvakeen Amala Doss Hebciba
AU  - Rajendran, Susai Santhammal
AU  - Lačnjevac, Časlav
AU  - Singh, Gurmeete
PY  - 2022
UR  - http://aspace.agrif.bg.ac.rs/handle/123456789/6059
AB  - Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a pre-processing procedure, is necessary for underwater vision. In this paper, introduced the Deep learning based Underwater Metal object detection using input Image data by using several step to improve the model performance. In this experimentation we are using TURBID dataset 100 images to validate the performance. And also we compare the performance result by given the input images in different validation level. In first input image is initially preprocessed and that images is given to the KFCM-Segmentation. The segmented images are given to the DWT Extraction to extract the features from those images. And finally the Convolution Neural Network (CNN) is used to classify the images to detect the objects. Also this proposed model attained the classification accuracy of 98.83%. This method is much suitable for detect the objects in underwater robotically. Metallic parts of machines of ships or aero planes may submerge in sea water. They may undergo corrosion when they come in contact with sea water which contains 3.5% sodium chloride. This is most commonly responsible for the corrosive nature of the sea water. The robots made of materials such as mild steel may also undergo corrosion when they come in contact with sea water, while is search. If a paint coating is given, it will control the corrosion of these proposed materials. Hence this work is undertaken. Mild steel is coated with Asian guard red paint. Corrosion resistance of mild in3.5% sodium chloride solution is measured before coating and after coating by electrochemical studies such as such as polarization study and AC impedance spectra. The corrosion inhibition efficiency offered by red paint to mild steel in 3.5% sodium chloride is 99.98%.
PB  - Engineers Society for Corrosion
T2  - Materials Protection
T1  - Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data
T1  - DETEKTOVANJE PODVODNIH METALNIH OBJEKATA POMOĆU VEŠTAČKE INTELIGENCIJE I ZAŠTITA OD KOROZIJE PREDMETA OD MEKOG ČELIKA KORIŠĆENIH U PODVODNOJ STUDIJI-STUDIJA SLUČAJA Deo A-detektovanje podvodnih metalnih objekata pomoću veštačke inteligencije
EP  - 14
IS  - 1
SP  - 5
VL  - 63
DO  - 10.5937/zasmat2201005R
ER  - 
@article{
author = "Rajendran, Dorothy and Sasilatha, Thankappan and Mary, Suvakeen Amala Doss Hebciba and Rajendran, Susai Santhammal and Lačnjevac, Časlav and Singh, Gurmeete",
year = "2022",
abstract = "Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a pre-processing procedure, is necessary for underwater vision. In this paper, introduced the Deep learning based Underwater Metal object detection using input Image data by using several step to improve the model performance. In this experimentation we are using TURBID dataset 100 images to validate the performance. And also we compare the performance result by given the input images in different validation level. In first input image is initially preprocessed and that images is given to the KFCM-Segmentation. The segmented images are given to the DWT Extraction to extract the features from those images. And finally the Convolution Neural Network (CNN) is used to classify the images to detect the objects. Also this proposed model attained the classification accuracy of 98.83%. This method is much suitable for detect the objects in underwater robotically. Metallic parts of machines of ships or aero planes may submerge in sea water. They may undergo corrosion when they come in contact with sea water which contains 3.5% sodium chloride. This is most commonly responsible for the corrosive nature of the sea water. The robots made of materials such as mild steel may also undergo corrosion when they come in contact with sea water, while is search. If a paint coating is given, it will control the corrosion of these proposed materials. Hence this work is undertaken. Mild steel is coated with Asian guard red paint. Corrosion resistance of mild in3.5% sodium chloride solution is measured before coating and after coating by electrochemical studies such as such as polarization study and AC impedance spectra. The corrosion inhibition efficiency offered by red paint to mild steel in 3.5% sodium chloride is 99.98%.",
publisher = "Engineers Society for Corrosion",
journal = "Materials Protection",
title = "Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data, DETEKTOVANJE PODVODNIH METALNIH OBJEKATA POMOĆU VEŠTAČKE INTELIGENCIJE I ZAŠTITA OD KOROZIJE PREDMETA OD MEKOG ČELIKA KORIŠĆENIH U PODVODNOJ STUDIJI-STUDIJA SLUČAJA Deo A-detektovanje podvodnih metalnih objekata pomoću veštačke inteligencije",
pages = "14-5",
number = "1",
volume = "63",
doi = "10.5937/zasmat2201005R"
}
Rajendran, D., Sasilatha, T., Mary, S. A. D. H., Rajendran, S. S., Lačnjevac, Č.,& Singh, G.. (2022). Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data. in Materials Protection
Engineers Society for Corrosion., 63(1), 5-14.
https://doi.org/10.5937/zasmat2201005R
Rajendran D, Sasilatha T, Mary SADH, Rajendran SS, Lačnjevac Č, Singh G. Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data. in Materials Protection. 2022;63(1):5-14.
doi:10.5937/zasmat2201005R .
Rajendran, Dorothy, Sasilatha, Thankappan, Mary, Suvakeen Amala Doss Hebciba, Rajendran, Susai Santhammal, Lačnjevac, Časlav, Singh, Gurmeete, "Deep learning based underwater metal object detection using input image data and corrosion protection of mild steel used in underwater study-A case study Part A-Deep learning based underwater metal object detection using input image data" in Materials Protection, 63, no. 1 (2022):5-14,
https://doi.org/10.5937/zasmat2201005R . .

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