Monitoring ambient vibration pollution based on visual information perception and neural network analysis

摘要

As a type of pollution, low-frequency vibrations in the environment have a negative effect on human life. To monitor pollution without extra complications, we propose a novel method based on visual information perception and neural network analysis. Image frame sequences can capture the state of objects, and changes in pixel values at salient vibration points may indirectly reflect ambient vibrations. In this paper, a test environment is established to simulate the influence of vibrations on daily life. A CCD camera is used to continuously sample objects via image frame sequences to monitor potential vibration pollution in the current environment through image processing and neural network analysis. During the image processing stage, a combination of image filtering and edge extraction can accurately locate the test objects' contours. During the low-frequency vibration monitoring stage, the neural network analyzes the changes in pixel values at the contour points to monitor ambient vibration pollution. The network combines local and global features for vibration frequency classification and prediction. The proposed method's superiorly is verified by the test results and a comparison of four different networks. The results demonstrate that this method accurately locates salient vibration points to monitor ambient vibration pollution.