TY - JOUR
T1 - Design of an Active Laser Mini-Camera Detection System Using CNN
AU - Liu, Chun
AU - Zhao, Changming
AU - Zhang, Haiyang
AU - Zhang, Zilong
AU - Zhai, Yanwang
AU - Zhang, Yali
N1 - Publisher Copyright:
© 2009-2012 IEEE.
PY - 2019/12
Y1 - 2019/12
N2 - The growing popularity of the mini-camera is posing a serious threat to privacy and personal security. Disguised as common tools in rooms, these devices can become undetectable. Moreover, conventional active laser detection systems often fail to recognize them owing to their small lens size, weak reflectivity, and the influence of interference targets. In this paper, a method for building a laser active detection system for mini-cameras is proposed. Using a monostatic optical system and a deep learning classification algorithm, this anti-camera system can detect mini-cameras accurately in real time. This article describes the system components including its optical design, core components and image processing algorithm. The capability of the system for detecting mini-cameras and identifying interference is also experimentally demonstrated. This work successfully overcomes the limit of mini-camera detection using deep learning methods in active laser detection systems.
AB - The growing popularity of the mini-camera is posing a serious threat to privacy and personal security. Disguised as common tools in rooms, these devices can become undetectable. Moreover, conventional active laser detection systems often fail to recognize them owing to their small lens size, weak reflectivity, and the influence of interference targets. In this paper, a method for building a laser active detection system for mini-cameras is proposed. Using a monostatic optical system and a deep learning classification algorithm, this anti-camera system can detect mini-cameras accurately in real time. This article describes the system components including its optical design, core components and image processing algorithm. The capability of the system for detecting mini-cameras and identifying interference is also experimentally demonstrated. This work successfully overcomes the limit of mini-camera detection using deep learning methods in active laser detection systems.
KW - Mini-camera
KW - active laser detection system
KW - deep learning classification
KW - interference target.
UR - http://www.scopus.com/inward/record.url?scp=85077212690&partnerID=8YFLogxK
U2 - 10.1109/JPHOT.2019.2957521
DO - 10.1109/JPHOT.2019.2957521
M3 - Article
AN - SCOPUS:85077212690
SN - 1943-0655
VL - 11
JO - IEEE Photonics Journal
JF - IEEE Photonics Journal
IS - 6
M1 - 8922785
ER -