Video monitoring equipment fault automatic detection method

Abstract

The invention discloses a video monitoring equipment fault automatic detection method which includes the following steps: a convolutional auto-encoder uses different numbers of network layers and different numbers of hidden nodes to extract the features of a monitoring image, image feature extraction is carried out in a pooling layer, and the convolutional auto-encoder is iterated until the accuracy of a classification model converges; a convolutional neural network uses the features of the monitoring image extracted by the convolutional auto-encoder as the basis of image classification to realize supervised learning; and the convolutional neural network uses a parallel computing architecture to train a network model, and after training of the network model, the model is applied to the classification process of a test image, and the damage condition of video monitoring equipment is judged according to the classification result. According to the video monitoring equipment fault automatic detection method of the invention, a corresponding improved algorithm is put forward for video monitoring equipment fault detection, and an automatic and fast fault detection method is put forward.

Description

technical field [0001] The invention belongs to the field of deep learning algorithms, and in particular relates to an automatic fault detection method for video monitoring equipment. Background technique [0002] The fault detection of traditional video surveillance equipment is mainly realized by the sensor of the machine equipment. However, in the actual detection, the sensor itself will have a fault, which makes it impossible to check the fault condition of the video equipment in time. The invention detects the failure of the video monitoring equipment by adopting the monitoring image classification method. Surveillance image classification refers to dividing the screenshots reserved in the monitoring process into 4 categories according to features such as black screen, color cast, normal and occlusion. Its main purpose is to judge the damage of monitoring equipment according to the classification result. The classification of images requires enough training samples, b...

AI Technical Summary

Problems solved by technology

Compared with these unsupervised learning models, ordinary autoencoders cannot effectively solve the problem of pooling and whitening in image data, and a large number of redundant parameters will be generated during training, which reduces the computational efficiency.

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