Paper Title
IMAGE SELECTION USING SELF SERVICE VISUAL ANALYTICS FOR UXO DETECTION
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Authors
Keywords
YOLO, Deep Learning, Image Processing, ROBOFLOW dataset, YAML File, Tensor Flow
Abstract
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.
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How To Cite (APA)
Sivabalan S, Abinaya S, Gayathri A, Jayashree C, & Rathika M (April-2024). IMAGE SELECTION USING SELF SERVICE VISUAL ANALYTICS FOR UXO DETECTION. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), f662-f666. https://ijnrd.org/papers/IJNRD2404580.pdf
Issue
Volume 9 Issue 4, April-2024
Pages : f662-f666
Other Publication Details
Paper Reg. ID: IJNRD_218600
Published Paper Id: IJNRD2404580
Downloads: 000121997
Research Area: Computer EngineeringÂ
Author Type: Indian Author
Country: Perambalur, Tamil Nadu, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2404580.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404580
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