Detection of objects with diverse geometric shapes in GPR images using deep-learning methods

Abstract

Buried objects with regular geometric shapes appear as hyperbolic structures in ground-penetrating radar (GPR) images. The shapes of these hyperbolic structures differ depending on the geometric shapes of the objects. In this study, current deep learning-based object detection algorithms such as Faster R-CNN, YOLOv5, and single-shot detector are used to detect hyperbolic structures in GPR images and classify the buried object according to its geometric shape. A mixed data set is produced for training the models. A GPR measurement device is designed with a vector network analyzer and Vivaldi antenna pair to be used in laboratory measurements. Objects with rectangular and cylindrical geometric shapes are placed under the table and measurements are performed. The measurement scenarios created in the laboratory are modeled in the gprMax program and synthetic GPR data are produced. Data augmentation techniques such as flipping and resizing are applied to expand the data set. As a result of the training, three models successfully detect the objects and classify them according to their geometric shapes. The Faster R-CNN model gives the most accurate detection and classification with the metrics classification loss = 5.4 × 10−3, localization loss = 9 × 10−3, regularization loss = 5.1 × 10−5, [email protected] = 1, and [email protected]:0.95 = 1.

Keywords

• deep learning
• ground-penetrating radar
• object detection
• rectangular and cylindrical objects
• object classification