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Convolutional Neural Network Approach for Buried Target Recognition in FL-LWIR imagery

机译:卷积神经网络方法在FL-LWIR图像中的掩埋目标识别

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A convolutional neural network (CNN) approach to recognition of buried explosive hazards in forward-looking long-wave infrared (FL-LWIR) imagery is presented. The convolutional filters in the first layer of the network are learned in the frequency domain, making enforcement of zero-phase and zero-dc response characteristics much easier. The spatial domain representations of the filters are forced to have unit 12 norm, and penalty terms are added to the online gradient descent update to encourage orthonormality among the convolutional filters, as well smooth first and second order derivatives in the spatial domain. The impact of these modifications on the generalization performance of the CNN model is investigated. The CNN approach is compared to a second recognition algorithm utilizing shearlet and log-gabor decomposition of the image coupled with cell-structured feature extraction and support vector machine classification. Results are presented for multiple FL-LWIR data sets recently collected from US Army test sites. These data sets include vehicle position information allowing accurate transformation between image and world coordinates and realistic evaluation of detection and false alarm rates.
机译:提出了一种卷积神经网络(CNN)方法来识别前瞻性长波红外(FL-LWIR)图像中的埋藏爆炸危险。网络第一层中的卷积滤波器是在频域中学习的,这使得零相位和零直流响应特性的实施更加容易。滤波器的空间域表示被迫具有单位12范数,并且惩罚项被添加到在线梯度下降更新中以鼓励卷积滤波器之间的正交性,以及在空间域中平滑一阶和二阶导数。研究了这些修改对CNN模型的泛化性能的影响。将CNN方法与利用图像的剪切波和log-gabor分解以及细胞结构特征提取和支持向量机分类的第二种识别算法进行比较。呈现了最近从美国陆军测试地点收集的多个FL-LWIR数据集的结果。这些数据集包括车辆位置信息,可在图像和世界坐标之间进行精确转换,并对检测和误报率进行实际评估。

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