Please use this identifier to cite or link to this item:
http://hdl.handle.net/2080/4651| Title: | Classification of Ground penetrating radar data using YOLOv8 Model |
| Authors: | Goyal, Pramod Tarai, Sangeeta Maiti, Subrata Chongder, Prasun |
| Keywords: | Ground-Penetrating Radar YOLOv8 Landmine Detection Object Classification Machine Learning, gprMax |
| Issue Date: | Jul-2024 |
| Citation: | IEEE SPace, Aerospace and defenCE (SPACE), Bangalore, India, 22-23 July 2024 |
| 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 |
| Description: | Copyright belongs to proceeding publisher |
| URI: | http://hdl.handle.net/2080/4651 |
| Appears in Collections: | Conference Papers |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2024_SPACE_PGoyal_Classification.pdf | 1.13 MB | Adobe PDF | View/Open Request a copy |
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