Method for detecting fire accident on expressway or in tunnel based on video detection technology

Abstract

The invention relates to the technical field of traffic detection and discloses a method for detecting a fire accident on an expressway or in a tunnel based on a video detection technology. The method comprises the following steps: extracting a picture from an obtained video according to a preset frame rate; utilizing the obtained picture to establish a tunnel background model; extracting a foreground target image from the picture with a background differentiating method; dividing the picture into a background part and a foreground part, and respectively updating the background part and the foreground part in different updating rates; analyzing the foreground target of the image in a detection period, and judging whether a suspected static target exists or not according to time sequence information; and if the suspected static target exists, determining a suspected flame region in combination with the moving and static features of flames, and finally analyzing the suspected flame region of a sequentially extracted image according to flame judging rules and judging whether the fire accident exists or not. The method has the advantages of simple detection algorithm, small calculated amount, capability of shortening processing time, capability of improving detection efficiency, and capability of reducing the influence of the complicated scenic illumination condition of the tunnel on fire detection.

Description

technical field [0001] The invention relates to the technical field of traffic detection, in particular to a method for detecting fire incidents in expressway tunnels based on video detection technology. Background technique [0002] With the rapid development of economic construction, the operating mileage of expressways has increased rapidly, the number of vehicles passing through expressways has increased sharply, and the safety hazards of expressway tunnels have also increased. As an important part of the transportation system, expressway tunnels have particularly prominent traffic safety issues. Once a fire or other accident occurs in the tunnel, it will not only cause serious property losses, but may also threaten the lives of passengers. Therefore, it is of great significance to monitor expressway tunnel fire incidents and give early warning to maintain tunnel traffic safety. [0003] At present, commonly used fire monitoring technologies are divided into two categor...

AI Technical Summary

Problems solved by technology

(1) Based on the detection method of color and brightness, scholars have established different flame color models in different color spaces according to the color information of flames. In recent years, some scholars have also proposed to use machine learning methods to obtain flame color models; (2 ) method of combining color, movement, and flame structure, that is, based on the color characteristics of the flame, combined with information such as the law of motion of the flame and the spatial structure of the flame change for fire detection; (3) based on the method of video state analysis, scholars have passed A large number of experiments have found that the flickering frequency of flames ranges between 3-25Hz, generally 10Hz, and this feature can be used to distinguish flames from other objects; (4) methods such as neural networks and support vector machines are used for pattern recognition features. extraction and classification, but the technology is still in its infancy

[0005] Due to the complex lighting conditions of the tunnel scene, especially the reflections caused by car lights, lighting in the tunnel, and special materials on both sides of the tunnel, it has a great impact on fire detection. For fire incidents, if the early warning is not given in time, it will easily lead to serious traffic accidents, which requires high real-time performance of the algorithm

Although there are many existing fire detection methods, the existing general fire detection algorithms have shortcomings such as low real-time performance, high false alarm rate, and weak robustness, and the application effect in highway tunnel fire detection is not good.

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