Classification of Ground penetrating radar data using YOLOv8 Model

Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/4651

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DC Field	Value	Language
dc.contributor.author	Goyal, Pramod	-
dc.contributor.author	Tarai, Sangeeta	-
dc.contributor.author	Maiti, Subrata	-
dc.contributor.author	Chongder, Prasun	-
dc.date.accessioned	2024-08-14T07:20:51Z	-
dc.date.available	2024-08-14T07:20:51Z	-
dc.date.issued	2024-07	-
dc.identifier.citation	IEEE SPace, Aerospace and defenCE (SPACE), Bangalore, India, 22-23 July 2024	en_US
dc.identifier.uri	http://hdl.handle.net/2080/4651	-
dc.description	Copyright belongs to proceeding publisher	en_US
dc.description.abstract	Landmines pose a significant challenge to military operations and civilian safety. Traditional Ground-Penetrating Radar (GPR) systems struggle to identify contemporary plastic landmines due to their low reflectivity and similarity to benign objects. This research pioneers the integration of You Only Look Once version 8 (YOLOv8) object classification algorithm with GPR B-scan images, demonstrating notable advancements in subsurface object classification accuracy. By enhancing landmine detection, this study aims to bolster the safety and operational efficiency of the Indian Army in mine-contaminated terrains	en_US
dc.subject	Ground-Penetrating Radar	en_US
dc.subject	YOLOv8	en_US
dc.subject	Landmine Detection	en_US
dc.subject	Object Classification	en_US
dc.subject	Machine Learning,	en_US
dc.subject	gprMax	en_US
dc.title	Classification of Ground penetrating radar data using YOLOv8 Model	en_US
dc.type	Article	en_US
Appears in Collections:	Conference Papers

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