Automatic Target Detection Using Ground Penetrating Radar (GPR) Data Based on A Machine Learning Approach: A Survey
Year: 2024
Type: article
Abstract: Ground Penetrating Radar (GPR) is a valuable tool for subsurface exploration across diverse fields such as archaeology, defence, and civil engineering. Traditionally, GPR data has been interpreted manually, requiring extensive expertise and being time-consuming. Recently, machine- learning approaches have emerged as effective solutions for automating target detection, significantly reducing time and human dependency while enhancing accuracy. This review provides a comprehensive overview of the current advancements in automatic target detection using GPR data, focusing on machine learning-based methods. We summarize feature extraction techniques, popular machine learning algorithms, and challenges within this domain. This paper also identifies future directions and potential improvements for robust, accurate, and efficient GPR-based automatic target detection systems. Key Words: Machine Learning, Ground Penetrating Radar, Object Detection, Signal Processing.
DOI: 10.55041/ijsrem39975
Author: Gaurav R. Savade
Institution: Babasaheb Naik College of Engineering, Pusad
Language: English
Subfield: Ocean Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4405689701