Dangerous working area accident automatic detection and alarm method based on deep learning

Abstract

The invention discloses a dangerous working area accident automatic detection alarm method based on deep learning, and the method comprises the steps: obtaining original video data, carrying out the preprocessing, and converting a video into an input training set acceptable for a deep learning network; learning a feature mode in a training video through a convolutional space-time automatic encoderdecoder, and performing training optimization by using the training set to obtain a workshop accident detection model; and acquiring a real-time monitoring video to be detected, detecting the reconstruction error of each frame of monitoring video image by adopting the workshop accident detection model, and if the local minimum reconstruction error of a plurality of continuous real-time monitoringimages is greater than a threshold value, sending corresponding alarm information and corresponding monitoring position information to a workshop administrator terminal. On the basis of analysis of alarge number of videos, video special learning of a normal scene is carried out, a fully trained detection model is obtained, abnormal accidents in a workshop can be rapidly and accurately detected,and accident detection can be carried out in any workshop scene.

Description

technical field [0001] The invention belongs to the technical field of video content automatic analysis, and in particular relates to an automatic detection and alarm method for accidents in dangerous work areas based on deep learning. Background technique [0002] Workshop safety is not only the lifeline of the enterprise, but also the lifeline of employees. However, the production environment of modern workshops is becoming more and more complex, and the production process requires more precise operations. Long-term wear and tear or improper operation will cause very serious consequences such as equipment collapse, equipment entanglement, equipment explosion, etc., ranging from machine damage to workers may lose their lives. . On the one hand, with the continuous expansion of the scale of the monitoring system and the massive increase of video data, it is becoming more and more difficult to obtain useful information or intelligence from it. The search efficiency is low an...

AI Technical Summary

Problems solved by technology

However, the training of existing convolutional neural networks generally requires supervision and requires labels as learning signals. Video signals involve large data processing, and due to its high dimensionality, random noise, and the interaction of a large number of events, it is difficult to manually It is very difficult to detect, segment and mark the region, and the detection model obtained in this way is difficult to generalize to different surveillance scenarios

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