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

主动式毫米波成像(AMWI)技术是检测隐藏在衣服下的危险物体的有效方法.但AMWI获取的图像通常很模糊,而且一些隐匿物体的尺寸较小,因此隐匿物品的自动检测和定位仍然是一个具有挑战性的问题.姚家雄等1]首先使用卷积神经网络(CNNs)结合穷举滑动窗口方法来检测隐藏物体.做了两点改进:(1)使用上下文(背景)信息抑制干扰,(2)使用两步搜索方法代替穷举搜索来降低计算复杂度.首先在垂直方向上使用一个CNN来过滤干扰,得到隐藏物体的垂直位置,然后用另一个CNN来确定水平位置.为了充分利用上下文信息,使用IoG(交集和真值的比)代替IoU(交并比)来定义训练和测试过程中的正负样本.实验结果表明,该方法将计算时间减小到约30%,同时实现更好的检测性能.

A low-complexity method for concealed object detection in active millimeter-wave images

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. Yao1] first employed convolutional neural networks (CNNs) and used a dense sliding window method to detect concealed objects. In this paper, we make two improvements on Yao"s work: (1) Using contextual information to suppress interference and improve detection rate; (2) Using a two-step search method instead of exhaustive search to reduce computing complexity. We firstly use one CNN in vertical direction to filter the interference and get the vertical position of the concealed object, then use another CNN to determine the horizontal position. To make use of big window containing contextual information, we use IoG (intersection-over-ground-truth) instead of IoU (Intersection-over-Union) to define positive and negative samples in training and testing process. Experimental results show that our proposed method reduce the computing time to about 30% while achieving better detection performance.