Abstract
Passive millimeter wave has been employed in security inspection owing to a good penetrability to clothing and harmlessness. However, the passive millimeter wave images (PMMWIs) suffer from low resolution and inherent noise. The published methods have rarely improved the quality of images for PMMWI and performed the detection only based on PMMWI with bounding box, which cause a high rate of false alarm. Moreover, it is difficult to identify the low-reflective non-metallic threats by the differences in grayscale. In this paper, a method of detecting concealed threats in human body is proposed. We introduce the GAN architecture to reconstruct high-quality images from multi-source PMMWIs. Meanwhile, we develop a novel detection pipeline involving semantic segmentation, image registration, and comprehensive analyzer. The segmentation network exploits multi-scale features to merge local and global information together in both PMMWIs and visible images to obtain precise shape and location information in the images, and the registration network is proposed for privacy concerns and the elimination of false alarms. With the grayscale and contour features, the detection for metallic and non-metallic threats can be conducted, respectively. After that, a synthetic strategy is applied to integrate the detection results of each single frame. In the numerical experiments, we evaluate the effectiveness of each module and the performance of the proposed method. Experimental results demonstrate that the proposed method outperforms the existing methods with 92.35% precision and 90.3% recall in our dataset, and also has a fast detection rate.
Highlights
The millimeter wave band is a part of the electromagnetic spectrum ranging from30 GHz to 300 GHz
The passive millimeter wave images (PMMWIs) and VI are synthetically utilized for high-precision segmentation of human body and locations of metallic threats, and through the accurate registration between PMMWI and VI, false alarm is further removed
In terms of nonmetallic threats, which is tricky for PMMWI inspection system, we explored the contour feature of human body with deep neural networks and achieved the detection of large nonmetallic threats
Summary
The millimeter wave band is a part of the electromagnetic spectrum ranging from30 GHz to 300 GHz. The millimeter wave band is a part of the electromagnetic spectrum ranging from. Millimeter wave can find concealed threats behind the clothing through temperature distribution which is generated by electromagnetic radiation from the target. The AMMWI emits specific millimeter waves to the target, and the image is carried out by the synthesis of the reflected waves. The AMMWI can provide more detailed information and better image quality; active millimeter waves have radiation on human and AMMWI is less used in practical application. The PMMWI does not produce radiation to the human as it collects radiation from human to process. It has higher imaging rate than the AMMWI [6].
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