红外与毫米波学报2019，Vol.38Issue(1)：32-38,7.DOI:10.11972/j.issn.1001-9014.2019.01.006
一种用于主动式毫米波图像的低复杂度隐匿物品检测方法
A low-complexity method for concealed object detection in active millimeter-wave images
摘要
Abstract
Active millimeter wave imaging ( AMWI) is an efficient way to detect dangerous objects concealed under clothes. However, because the images acquired by AMWI are often obscure and some of concealed objects are small in size, the automatic detection and localization of the objects remain as a challenging problem. Yao[1]first employed convolutional neural networks ( CNNs) and used a dense sliding windowmethod to detect concealed objects. In this paper, the author presents two improvements over Yao 's work: 1) Using contextual information to suppress interference and improve detection probability;2) Using a two-step search method instead of exhaustive search to reduce the computational complexity. To reduce the computational complexity, the author first uses a CNN in vertical direction to filter the interference and obtain the vertical position of the concealed object, then uses another CNN to determine the horizontal position of the concealed object. To make use of big windowcontaining contextual information, the author uses IoG ( intersection-over-ground-truth) instead of IoU ( Intersection-over-Union) to define positive and negative samples in training and testing process. Experimental results showthat the proposed method will make the length of computational time reduced to about 30％ of that of the exhaustive search while achieving better detection performance.关键词
主动式毫米波图像/隐匿物品检测/卷积神经网络/上下文信息Key words
active millimeter-wave image/concealed object detection/CNN/contextual information分类
信息技术与安全科学引用本文复制引用
王崇剑,孙晓玮,杨克虎..一种用于主动式毫米波图像的低复杂度隐匿物品检测方法[J].红外与毫米波学报,2019,38(1):32-38,7.基金项目
Supported by National Natural Science Foundation of China(61731021) （61731021）
