论文解读《Deep Convolutional Neural Networks for Classifying GPR B

论文解读《Deep Convolutional Neural Networks for Classifying GPR B"/>

论文解读《Deep Convolutional Neural Networks for Classifying GPR B

标题：Deep Convolutional Neural Networks for Classifying GPR B-Scans
作者：Lance E. Besaw and Philip J. Stimac
期刊：Proc. of SPIE Vol. 9454 945413-1

abstract

Symmetric and asymmetric buried explosive hazards (BEHs) present real, persistent, deadly threats on the modern battlefield. Current approaches to mitigate these threats rely on highly trained operatives to reliably detect BEHs with reasonable false alarm rates using handheld Ground Penetrating Radar (GPR) and metal detectors. As computers become smaller, faster and more efficient, there exists greater potential for automated threat detection based on state-of-the-art machine learning approaches, reducing the burden on the field operatives.
Recent advancements in machine learning, specifically deep learning artificial neural networks, have led to significantly improved performance in pattern recognition tasks, such as object classification in digital images. Deep convolutional neural networks (CNNs) are used in this work to extract meaningful signatures from 2-dimensional (2-D) GPR B-scans and classify threats. The CNNs skip the traditional “feature engineering” step often associated with machine learning, and instead learn the feature representations directly from the 2-D data. A multi-antennae, handheld GPR with centimeter-accurate positioning data was used to collect shallow subsurface data over prepared lanes containing a wide range of BEHs. Several heuristics were used to prevent over-training, including cross validation, network weight regularization, and “dropout.” Our results show that CNNs can extract meaningful features and accurately classify complex signatures contained in GPR B-scans, complementing existing GPR feature extraction and classification techniques.

掩埋爆炸危险（BEH）在现代战场上有着致命的威胁。随着计算机的微型化和机器学习的快速发展，快速和智能的监测成为可能。本文结果显示，CNN能够提取有效特征，