Abstract
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.
Highlights
In recent years, miniature photoelectric devices have been utilized for a wide range of applications
This paper proposes an innovative method for building an anti-mini-camera system with a monostatic optical design and a convolutional neural network (CNN)-based classification algorithm
In order to overcome the difficulties in mini-camera detection, this study focuses on the optical system and the design of an algorithm to address the issues of weak signals and classification
Summary
Miniature photoelectric devices have been utilized for a wide range of applications. When detecting mini-cameras, there are several limitations: (1) The size of the lens aperture of a minicamera is so small that the reflection is too weak to stand in stark contrast with the background; (2) the tiny lens aperture gives rise to a diffraction effect that further spreads the energy; and (3) reflective objects or other light sources can influence the system and can be incorrectly identified as the target cameras. These limitations bring about a critical reduction of reflection energy and inevitable false negative (FN) and false positive (FP) alerts. It fills gaps in the field of engineering active laser MDSs
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