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)
Securing border zones poses a significant challenge for military forces, with the inability to
monitor continuously resulting in tragic incidents and loss of life among soldiers. The surveillance and
security of border zones remain critical concerns for military forces, with the challenging task of continuous
monitoring often leading to tragic incidents and casualties among deployed personnel. To address this pressing
issue, this project proposes an innovative approach leveraging the You Only Look Once (YOLO) deep
learning model for bomb detection. In response to escalating concerns regarding public safety and terrorism
threats, there is a growing demand for robust and efficient explosive detection systems. By harnessing the
capabilities of YOLO, known for its speed and accuracy in object detection tasks, this system aims to enhance
security measures along border zones. The proposed bomb detection system offers the potential to mitigate
risks and protect military personnel by providing real-time detection and alert mechanisms, thereby bolstering
national security efforts. Through the integration of advanced technology and machine learning algorithms,
this solution strives to enhance situational awareness and safeguard lives in critical border regions. By
deploying advanced machine learning algorithms, the system offers the potential to mitigate risks and prevent
security breaches, thereby safeguarding both military personnel and civilian populations. Through the
integration of cutting-edge technology and proactive security measures, this solution seeks to enhance
situational awareness and strengthen national defence capabilities in border protection effort.
"IMAGE SELECTION USING SELF SERVICE VISUAL ANALYTICS FOR UXO DETECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 4, page no.f662-f666, April-2024, Available :http://www.ijnrd.org/papers/IJNRD2404580.pdf
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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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