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基于视觉显著性的毫米波隐匿物品检测算法
2022年电子技术应用第11期
张珂绅,郭文风,王鹤澎,叶学义
杭州电子科技大学 通信工程学院,浙江 杭州310018
摘要:针对毫米波图像中隐匿物品与人体灰度差异小、形状多变的问题,提出了一种基于视觉显著性的隐匿物品检测算法。该算法在双边滤波后,结合OTSU和形态学运算完成预处理以获得人体区域,再根据频域显著性计算定位前景,经背景抑制后生成显著图完成检测。实验数据表明,所提算法与典型的主动式毫米波成像检测算法相比,检出率分别提高5.87%和9.08%,有更好的检测性能。
中图分类号:TN928;TP391.41
文献标识码:A
DOI:10.16157/j.issn.0258-7998.212492
中文引用格式:张珂绅,郭文风,王鹤澎,等. 基于视觉显著性的毫米波隐匿物品检测算法[J].电子技术应用,2022,48(11):99-103,109.
英文引用格式:Zhang Keshen,Guo Wenfeng,Wang Hepeng,et al. Millimeter wave hidden object detection algorithm based on visual saliency[J]. Application of Electronic Technique,2022,48(11):99-103,109.
Millimeter wave hidden object detection algorithm based on visual saliency
Zhang Keshen,Guo Wenfeng,Wang Hepeng,Ye Xueyi
School of Communication Engineering,Hangzhou Dianzi University,Hangzhou 310018,China
Abstract:In order to solve the problem that the gray level difference between hidden objects and human body in millimeter wave image is small and the shape is changeable, a hidden object detection algorithm based on visual saliency is proposed. After bilateral filtering, combined with OTSU and morphological operation,the algorithm completes the pretreatment to obtain the human body region. And then the foreground is located based on the significance calculation in the frequency domain. After background suppression,the significance map is generated,so the detection is completed. Experimental results show that compared with the typical active millimeter wave imaging detection algorithm, the detection rate of the proposed algorithm increases by 5.87% and 9.08%, respectively, and the proposed algorithm has better detection performance.
Key words :hidden object;visual salience;image signature;threshold segmentation

0 引言

近年来,机场、车站等公共场所的安全问题日益得到重视,对人体携带物品的安全检查必不可少。目前主流的安检手段如X射线探测虽然可以清晰成像,但X射线易电离且具有辐射性,不适合于人体安检[1]。毫米波辐射在电磁频谱中介于微波和红外线之间,属于非电离辐射,可以穿透人体衣物探测到隐匿物品,且对人体无害,有逐步取代传统安检手段的趋势[2]

随着前端成像技术的不断成熟,后端毫米波图像的隐匿物品检测成了目前亟待解决的问题。目前毫米波成像系统输出的图像分辨率较差,信噪比低,对目标检测性能有负面影响,容易造成误检和漏检[3]。这些都给隐匿物品检测带来了巨大的挑战。




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作者信息:

张珂绅,郭文风,王鹤澎,叶学义

(杭州电子科技大学 通信工程学院,浙江 杭州310018)




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