Deep anomaly detection in expressway based on edge computing and deep learning

In order to improve the real-time efficiency of expressway operation monitoring and management, the anomaly detection in intelligent monitoring network of expressway based on edge computing and deep learning is studied. The video data collected by the camera equipment in the intelligent monitoring network structure of the expressway is transmitted to the edge processing server for screening and then sent to the convolutional neural network. The convolutional neural network uses the multi-scale optical flow histogram method to preprocess the video data after the edge calculation to generate the training sample set and 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 intelligent monitoring network of expressway. The research method is used to detect the anomaly in an intelligent monitoring network of an expressway. The experimental results show that the method has better detection effect. The miss rate has reduced by 20.34% and 40.76% on average compared with machine learning method and small block learning method, respectively. The false positive rate has reduced by 27.67% and 21.77%, and the detection time is greatly shortened.

Automated summary

In this study, the real-time efficiency of expressway operation monitoring and management is improved using edge computing and deep learning. Anomaly detection in the intelligent monitoring network of expressway is achieved by transmitting video data collected by camera equipment to an edge processing server for screening and then sending it to a convolutional neural network. Experimental results show that the method has better detection effect, reducing the false positive rate and miss rate, and significantly shortening the detection time.