Detection of buried areas that can cause military threat with deep learning in ground penetreating radar images

The presence of buried landmines and improvised explosive devices is a serious threat in many areas around the world, especially for armies. Despite the fact that various techniques have been proposed and improved in the literature to detect and classify buried objects, automatic and easy to use systems providing high accuracy performance are still under research. Convolutional Neural Networks (CNNs) have recently shown outstanding performance on image classification and object detection tasks. The availability of large amount of data and improvement in hardware technology has accelerated the research in CNN, and in the recent times deep CNN architectures have been introduced. Moreover, CNNs are also used for the detection of buried objects with the help of ground penetrating radar (GPR). GPR is one of the most studied modalities for buried threat detection. The data used in thesis was collected using a vehicle mounted GPR system. The experimental data set comprised of 8664 positive and 8596 negative GPR images. This study proposes a novel CNN architecture for the detection of buried threats in GPR data. In the classification performed with the proposed CNN, 10-fold cross-validation was used and an average overall accuracy of 98 percent was obtained. The proposed CNN architecture has also been compared with different CNN architectures known in the literature. In addition, the advantages and disadvantages of the proposed architecture are revealed by experimenting the architectural filter sizes, optimization types, dropout rates and auto encoder downsampling features.