Tunnel vehicle indicator and illuminating lamp fault identification method

Abstract

The invention relates to the field of tunnel monitoring. The invention particularly relates to a tunnel vehicle indicator and an illuminating lamp fault identification method. The method comprises thefollowing steps that: firstly, a region of interest is extracted from a tunnel video stream image, then graying binary processing is carried out on the image of the extracted region, morphological operation is carried out on a binary image, then a vehicle indicator region is detected according to the color and shape characteristics of a vehicle indicator, and other regions serve as illuminating lamp candidate regions; the detected state of the area of the automobile finger lamp is identified, an automobile indicator fault identification model is established in combination with an input signalof the automobile finger lamp, and a fault condition is output; and HOG features of the lighting lamp candidate area are extracted, image training is carried out by combining an SVM classifier, so that a short-distance illuminating lamp area is effectively detected, a long-distance illuminating lamp area is detected according to illuminating lamp mounting position characteristics, the detected illuminating lamp state is identified, and an illuminating lamp fault identification model is established by combining an input signal of the illuminating lamp state.

Description

technical field [0001] The invention relates to the field of tunnel monitoring, in particular to a fault identification method for a tunnel car pointer and lighting lamps. Background technique [0002] With the continuous increase of highway tunnels, the number of electromechanical equipment in the tunnels is huge and various. In particular, lighting and car pointers in the tunnel occupy a considerable proportion in the total number of mechanical and electrical equipment in the tunnel, and their importance and maintenance work are self-evident. In order to effectively maintain complex electromechanical equipment and keep it in an efficient working state at all times, it is far from enough to rely solely on manual inspections and regular maintenance, and cannot meet the requirements in terms of maintenance efficiency and maintenance effect. [0003] Traditional video monitoring requires monitoring personnel to watch the video non-stop. Traditional video monitoring can only ...

AI Technical Summary

Problems solved by technology

Traditional video monitoring can only provide simple functions of image capture, storage and playback, and it is difficult to play the role of early warning and alarm, and watching videos for a long time can easily lead to fatigue

In addition, as the number of surveillance cameras grows faster and the coverage area becomes wider and wider, it is often dizzying and it is difficult to respond to abnormalities in a timely manner.

[0004] At present, the state monitoring technology of electromechanical equipment mainly uses sensors to collect data of electromechanical components, and predicts the operating status of electromechanical equipment through statistical analysis of various data. This method can only partially understand the operating status of electromechanical equipment, and cannot monitor the status of equipment intuitively. Running status, which limits the operation and maintenance personnel to make quick judgments on faults

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