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Real-time dangerous objects detection in millimeter wave images
Author(s): Sheng Wang; Zuochang Ye; Yan Wang
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Paper Abstract

Millimeter wave imaging technology has been used in human body security inspection in public. Comparing to traditional X-ray imaging, it is more efficient and without harm to human body. Thus an automatic dangerous objects detection for millimeter wave images is useful to greatly save human labor. However, due to technology limitation, millimeter wave images are usually low resolution and with high noise, thus the dangerous objects hidden in human body are hard to be found. In addition, the detection speed is of great significance in practice. This paper proposes an efficient method for dangerous objects detection for millimeter wave images. It is based on a single unified CNN (Convolutional Neural Network). Compared to traditional region-based method like RCNN, by setting some default anchors over different aspect ratios at the last feature map, it is able to frame object detection as a regression problem to these anchors while predicting class probabilities. The model gets 70.9 mAP at 50 frames per second in millimeter wave images dataset, obtaining better performance than other method, showing a promise in practical using in the future.

Paper Details

Date Published: 9 August 2018
PDF: 6 pages
Proc. SPIE 10806, Tenth International Conference on Digital Image Processing (ICDIP 2018), 108060Z (9 August 2018); doi: 10.1117/12.2502985
Show Author Affiliations
Sheng Wang, Tsinghua Univ. (China)
Zuochang Ye, Tsinghua Univ. (China)
Yan Wang, Tsinghua Univ. (China)

Published in SPIE Proceedings Vol. 10806:
Tenth International Conference on Digital Image Processing (ICDIP 2018)
Xudong Jiang; Jenq-Neng Hwang, Editor(s)

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