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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

dc.creatorRajendran, Dorothy
dc.creatorSasilatha, Thankappan
dc.creatorMary, Suvakeen Amala Doss Hebciba
dc.creatorRajendran, Susai Santhammal
dc.creatorLačnjevac, Časlav
dc.creatorSingh, Gurmeete
dc.date.accessioned2022-04-15T08:39:05Z
dc.date.available2022-04-15T08:39:05Z
dc.date.issued2022
dc.identifier.issn0351-9465
dc.identifier.urihttp://aspace.agrif.bg.ac.rs/handle/123456789/6059
dc.description.abstractDue 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%.sr
dc.language.isoensr
dc.publisherEngineers Society for Corrosionsr
dc.rightsopenAccesssr
dc.rights.urihttps://creativecommons.org/licenses/by-nd/4.0/
dc.sourceMaterials Protectionsr
dc.subjectAC impedance spectrasr
dc.subjectConvolutional Neural Network (CNN)sr
dc.subjectFuzzy c-means clustering and TURBID dataset polarization studysr
dc.subjectinput Image datasr
dc.subjectsea watersr
dc.titleDeep 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 datasr
dc.titleDETEKTOVANJE 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 inteligencijesr
dc.typearticlesr
dc.rights.licenseBY-NDsr
dc.citation.epage14
dc.citation.issue1
dc.citation.rankM51
dc.citation.spage5
dc.citation.volume63
dc.identifier.doi10.5937/zasmat2201005R
dc.identifier.fulltexthttp://aspace.agrif.bg.ac.rs/bitstream/id/23630/Deep_learning_based_underwater_pub_2022.pdf
dc.identifier.scopus2-s2.0-85127200967
dc.type.versionpublishedVersionsr


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