Deep convolutional neural network-based stockyard smoke monitoring and online model updating method

Abstract

The invention relates to a deep convolutional neural network-based stockyard smoke monitoring and online model updating method. The method comprises the following steps of a, converting a smoke video into an image sequence by using a tool, and conducting the smoke labeling operation; b, by using the deep convolutional neural network, obtaining the more abstract high-level features of the smoke, iteratively training and optimizing model parameters, evaluating an iterative model according to a loss function and selecting an optimal model; c, according to the false-information and misinformation condition, updating the model in the online manner. According to the technical scheme of the invention, the effective feature extraction for smoke images is conducted based on the deep convolutional neural network. Based on the method, the smoke monitoring and the model updating are conducted in real time in the video monitoring condition. Therefore, the detection accuracy is further improved. The method is simple in operation, fast, effective, and high in robustness.

Description

technical field [0001] The invention relates to a smoke monitoring method, in particular to a stack yard smoke monitoring and online model updating method based on a deep convolutional neural network. Background technique [0002] Fire is one of the most common and important disasters that endanger public safety and social development. Fire not only destroys material property, but also seriously threatens people's life, health and safety. Once it happens, it will cause us irreparable losses. On August 12, 2015, a fire and explosion accident occurred in Tianjin Port, killing 165 people and injuring 798. The approved direct economic loss reached 6.866 billion yuan. The safety of the storage yard, especially the fire warning has always been an important topic in the field of fire safety. If the fire can be detected and extinguished in the early stage of the fire, the harm caused by the fire can be minimized. "Smoke is the beginning of fire", smoke is usually produced in the ...

AI Technical Summary

Problems solved by technology

On the one hand, it is difficult to remove the interference of smoke-like objects when using these methods, such as the interference of clouds and fog when using color features.

On the other hand, these aspects cannot describe the nature of smoke well, and the adaptability to environmental changes is not strong, such as the impact of light on motion detection, and the applicability of the selected features to different yard environments cannot be ensured.

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