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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Underwater Surface Target (Object Detection) Through Sonar Using ML Algorithms |
| Authors Name: | Pratiksha Navnath Walke , Vaishanavi Vishawanath Tupe , Diya Altaph Shaikh , Tejeshwini Sanjay Adsul |
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IJNRD_213589
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| Published Paper Id: | IJNRD2402083 |
| Published In: | Volume 9 Issue 2, February-2024 |
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| Abstract: | In underwater environments, the detection and recognition of submerged objects or targets play a crucial role in applications ranging from marine research to naval operations and underwater robotics. This project introduces an innovative approach to enhance the accuracy and efficiency of underwater target detection through the utilization of sonar technology and advanced machine learning algorithms. The project leverages the capabilities of sonar systems to emit sound waves into the underwater environment and receive their echoes, creating acoustic images of underwater surfaces and objects. These acoustic images are rich in information but often challenging to interpret accurately. To address this challenge, state-of-the-art machine learning algorithms, including deep learning techniques, are employed for the automatic detection and classification of underwater legitimate or phishing objects. The system's architecture involves the integration of sonar data acquisition, pre-processing, and feature extraction, followed by the application of machine learning models trained on diverse underwater object datasets. By utilizing deep neural networks and other ML techniques, the system learns to recognize and classify various underwater objects, such as Torpedo’s, Weapons, submarines, marine life, and geological formations. The benefits of this project extend to numerous domains, including marine conservation, underwater archaeology, and defense applications, where precise and rapid underwater object detection is essential. By combining sonar technology and machine learning algorithms, this project contributes to advancing our understanding and exploration of underwater environments, ultimately improving the safety and efficiency of various underwater operations. |
| Keywords: | Underwater Mines, Supervised, Classification Algorithms, Prediction Model. Machine Learning, Deep Learning, Sonar, etc. |
| Cite Article: | "Underwater Surface Target (Object Detection) Through Sonar Using ML Algorithms ", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 2, page no.a738-a741, February-2024, Available :http://www.ijnrd.org/papers/IJNRD2402083.pdf |
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| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2402083 Registration ID: 213589 Published In: Volume 9 Issue 2, February-2024 DOI (Digital Object Identifier): Page No: a738-a741 Country: shrigonda , maharastra, India Research Area: Computer Engineering Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2402083 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2402083 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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