Design of an Active Laser Mini-Camera Detection System Using CNN

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 minicameras 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.

Photoelectric Target Detection Algorithm Based on NVIDIA Jeston Nano

“…For instance, Ke [11] designed a fully automatic camera detection and recognition system, which combines machine learning and neural network methods to identify surveillance camera equipment effectively. Liu [12] introduced a photoelectric target recognition algorithm and a detection system based on convolutional neural networks to detect indoor micro-cameras using classified networks. Huang [13] used the improved YOLOv3 model to identify micro-cameras in a single frame.…”