Monitoring image anomaly detection based on lightweight network MobileNet

Qi Zhou; Xiancheng Feng; Zehao Bao; Yang Li

doi:10.1117/12.2652314

10 November 2022 Monitoring image anomaly detection based on lightweight network MobileNet

Qi Zhou, Xiancheng Feng, Zehao Bao, Yang Li

Proceedings Volume 12331, International Conference on Mechanisms and Robotics (ICMAR 2022); 123313E (2022) https://doi.org/10.1117/12.2652314
Event: International Conference on Mechanisms and Robotics (ICMAR 2022), 2022, Zhuhai, China

Abstract

At present, there are problems in remote video surveillance that it is difficult to detect image anomalies in complex scenes, and traditional algorithm functions are single (only for a certain specific scene or a certain type of abnormal image). Moreover, the currently commonly used convolutional neural network is deployed on the monitoring device, there are problems such as insufficient device memory and low computing power. In response to the above problems, this paper proposes a recognition method that applies the MobileNet convolutional neural network to the detection of abnormal images in surveillance video. The off-line enhancement method is used to reverse and translate the abnormal images in the surveillance video. The data set can be reasonably expanded, the problem of data imbalance can be solved, and the detection accuracy and generalization performance of abnormal images can be improved. The results show that this method can detect abnormalities such as freeze, overexposure, blur, and colorific distortion in remote video surveillance, with an accuracy rate of 91.75%, and can reduce the amount of model parameters and facilitate model deployment.

Citation Download Citation

Qi Zhou, Xiancheng Feng, Zehao Bao, and Yang Li "Monitoring image anomaly detection based on lightweight network MobileNet", Proc. SPIE 12331, International Conference on Mechanisms and Robotics (ICMAR 2022), 123313E (10 November 2022); https://doi.org/10.1117/12.2652314

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available