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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21524
Article Published : 8515
Total Authors : 22403
Total Reviewer : 805
Total Countries : 158
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| Paper Title: | Underwater Object Detection Based On Improved CNN |
| Authors Name: | Rakshitha M V , Samrudh R , Prakruthi P S , Nagaveni B. Nimbal |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_186298
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| Published Paper Id: | IJRTI2304247 |
| Published In: | Volume 8 Issue 4, April-2023 |
| DOI: | |
| Abstract: | — Detection and analyzing underwater noise is crucial for companies working in the marine industry. The CNN model employs single forward propagation through a neural network to detect objects in real-time, that is the entire image is predicted in a single algorithm run for training and validation. Thus, to overcome the overfitting problem due to these inherent problems in the domain-specific dataset, the CNN model pre-trained by the public image dataset is usually adopted for its fine-tuning. For example, an autonomous ship equipped with an Automatic Identification System (AIS) requires safe navigation, which is achieved by the detection of surrounding objects. Therefore, this study aimed to develop a new optimized model using one of the network architectures for deep learning features that would learn automatically from the input data, eliminating the requirements and engineering effort. Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. In this work, we propose a solution for underwater object detection that uses a combination of Convolutional Neural networks and specific image pre-processing steps. The goal is to classify underwater objects into biodegradable and non-biodegradable. |
| Keywords: | Detecting underwater object and classifying objects into Biodegradable or Non biodegradable.. |
| Cite Article: | "Underwater Object Detection Based On Improved CNN", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 4, page no.1503 - 1511, April-2023, Available :http://www.ijrti.org/papers/IJRTI2304247.pdf |
| Downloads: | 000205339 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2304247
Registration ID:186298
Published In: Volume 8 Issue 4, April-2023
DOI (Digital Object Identifier):
Page No: 1503 - 1511 Country: Bangalore, Karnataka, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2304247 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2304247 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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