显著性视觉的毫米波分区域检测算法

Detection of millimeter wave objects with visual saliency

针对毫米波图像中人体不同部位呈现不同的结构特性、隐匿物品与人体灰度差异小、检测困难的问题,提出了一种显著性视觉的毫米波分区域检测算法。该算法在双边滤波后,结合大津法(OTSU)和形态学运算完成预处理以获得人体区域,再根据人体特征进行区域划分,分区域采用对称判定策略判断目标是否存在,最后显著性增强并通过K-means聚类分割算法分割出隐匿物品完成检测。实验数据表明,所提算法与典型的主动式毫米波图像检测算法相比,检出率提高了8.28%,误报率减少了1.58%,有更好的检测性能。

Aiming at the problem that different parts of human body present different structural characteristics in millimeter wave image,the gray difference between hidden objects and human body is small,and detection is difficult,a millimeter wave segmen‐tation detection algorithm based on saliency vision is proposed.After bilateral filtering,the algorithm combined with OTSU and morphological operation to complete the pretreatment to obtain human body region,and then divided the region according to hu‐man body characteristics,using the symmetry decision strategy to determine whether the target exists in the region,and finally en‐hanced significance and K-means clustering segmentation algorithm to segment hidden items to complete detection.Experimental results show that compared with the typical active millimeter wave image detection algorithm,the detection rate of the proposed algorithm is improved by 8.28%,and the false positive rate is reduced by 1.58%,showing better detection performance.