DAD: Deep Anomaly Detection for Intelligent Monitoring of Expressway Network

Abstract

In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network (IMN) of expressway based on edge computing and deep learning is studied. The video data collected by the camera equipment in the IMN structure of the expressway is transmitted to the edge processing server for screening and then sent to the convolution neural network. Then video data was preprocessed after the edge calculation to generate the training sample set, then send it to the AlexNet model for feature extraction. SVM classifier model is used to train the feature data set and input the features of the test samples into the trained SVM classifier model to realize the anomaly detection in the IMN of expressway. The experimental results showed that the method has better detection effect than the machine learning method and the small block learning method, and the detection time is greatly shortened.