Prediction of Size of Buried Objects using Ground Penetrating Radar and Machine Learning Techniques

Barkataki, Nairit; Mazumdar, Sharmistha; Talukdar, R.; Chakraborty, Priyanka; Tiru, Banty; Sarma, Utpal

doi:10.1109/compe49325.2020.9200094

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Cited by 4 publications

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Classification Size of Underground Object from Ground Penetrating Radar Image using Machine Learning Technique

Azalan,

Esian,

Ali

et al. 2023

J. Phys.: Conf. Ser.

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Ground Penetrating Radar (GPR) is a useful tool in detecting subsurface object or hidden structure defects However, the time-consuming problems and high requirement of professional manpower is required to analyse the GPR data. Machine learning is a tool that endowed with the ability to learn, and it can reduce time taken for the GPR data analysing. To simplify the identification process, a framework is proposed to classify the size of underground metallic pipe by using Histogram of Oriented Gradient (HOG) as a feature extraction algorithm. Two machine learning algorithms namely Support Vector Machines (SVM) and Backpropagation Neural Network were proposed to classify the size of the underground metallic pipe. As a result, the accuracy from the identification is more than 98% for both classifier algorithm.

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