Some criminals use cameras to steal secrets and privacy in order to make huge profits. The improper use of cameras has brought great losses to the country and the people. However, the current camera detection method based on the cat-eye effect is easily affected by the reflection of the background, resulting in false alarms and affecting the discrimination of the target. In order to improve the accuracy of cat-eye target recognition in complex backgrounds, this paper proposes a new method of cat-eye target recognition based on the fusion of dual-spectral imaging and deep learning according to the imaging characteristics of cat-eye effect echo, background and noises. The method of dual-spectral frame difference is used to filter out the background interference. Then we combine the knowledge of machine vision and deep learning to realize the cat-eye target recognition through the recognition threshold and machine discrimination. An experimental system is established in this paper. The result shows that this method can effectively suppress background interference in the recognition of close-range cat-eye targets, and has the advantages of high detection accuracy and fast recognition speed.
Based on the theoretical model and simulation, this paper presents the acoustic detective sensitivity of three types of optical fiber acoustic sensors – RIM-FODS (reflective intensity modulated fiber optic displacement sensor), extrinsic F-P (Fabry-Perot) interferometer and all-fiber photoelastic interference sensor. Three designed optical fiber acoustic sensor systems are implemented to analyze the performance of each type and tested to investigate the detective sensitivity at 1kHz 94dB SPL (sound pressure level). The experimental results are in good agreement with those obtained by theoretical analysis and simulation.
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