CN113409535A - Tunnel flame detection system based on computer vision and artificial intelligence algorithm - Google Patents
Tunnel flame detection system based on computer vision and artificial intelligence algorithm Download PDFInfo
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Abstract
The invention belongs to the technical field of highway tunnel operation management, in particular to a tunnel flame detection system based on computer vision and artificial intelligence algorithm, which comprises a flame detection system and a data management platform: the flame detection system comprises an infrared module, an ultraviolet module and a camera module, wherein the infrared module adopts three infrared sensors sensitive to infrared rays, one infrared sensor is used for flame detection, the other two infrared sensors are respectively used for detecting background infrared radiation, the ultraviolet module converts flame narrow spectrum signals into electric pulse signals by using a solar blind type ultraviolet sensor (UV 185-260 nm), then the electric pulse signals are input into a calculation chip for calculation and processing, the camera module is used for monitoring and shooting hardware for an outdoor road, and a CNN + LSTM-based structured data space-time model fire detection neural network is operated.
Description
Technical Field
The invention relates to the technical field of highway tunnel operation management, in particular to a tunnel flame detection system based on computer vision and an artificial intelligence algorithm.
Background
The construction and management of the road tunnel enters into a repeat period. In the management of highway tunnel operation, tunnel fire is a type of safety accident with the greatest harm. In case of fire accident of highway tunnel, it will not only cause serious injury to driver and passengers in tunnel, but also will cause huge damage to tunnel lining structure, tunnel pavement and tunnel facilities. At the initial stage of a fire on a highway tunnel, whether drivers and passengers can safely evacuate depends on 2 time factors, namely the time required for the fire to develop into a danger for the drivers and passengers and the time required for the drivers and passengers to evacuate to a safe area. Only if the safe evacuation time is longer than the time required for evacuation to a safe area, the highway tunnel can be considered to be safe for the evacuation of the driver and passengers after the fire occurs. If the early flame of the fire of the highway tunnel can be effectively detected and early warning is carried out, the fire early warning device is not only beneficial to the safe evacuation of drivers and passengers, but also beneficial to the timely starting of fire-fighting equipment to inhibit the spread of fire, and the casualties and property loss caused by the fire are reduced to the maximum extent. In order to ensure the safe operation of the highway tunnel, it is necessary to timely identify and alarm all suspicious flames appearing in the tunnel.
The highway tunnel fire is mainly caused by various factors such as the failure of an electric line or equipment in the tunnel, the electromechanical failure of a vehicle, the traffic accident of an automobile, the ignition of a vehicle-mounted combustible or combustible material and the like. The traditional temperature-sensing fire flame detector is easily influenced by factors such as monitoring space area, height, dust, air velocity and the like, and is not beneficial to timely and effective detection of flame at the initial stage of tunnel fire. In the existing tunnel and subway fire alarm systems, the most applied fire detector products are mainly divided into two categories, namely a point type flame detector and a linear temperature-sensing fire detector. The point-type flame detectors are classified into infrared and ultraviolet flame detectors according to the spectral range. The principle is to judge whether the ignition point exists or not by detecting the flame distribution spectrum. The biggest problem in tunnel applications is the susceptibility to false alarms caused by interference from construction light sources such as sodium lamps, mercury lamps and electric welding arcs. The linear temperature-sensing fire detector comprises three types, namely a cable type, an air pipe type and an optical fiber temperature-sensing detector. The principle is that an alarm signal is generated by sensing the change of the ambient temperature. The main problem of the application in tunnels and subways is that effective alarming can not be carried out at the initial stage of fire occurrence.
With the development of artificial intelligence technology and the progress of image algorithm technology, the processing and understanding of video images gradually become the mainstream research direction of computer vision, the flame detection technology based on videos is more and more emphasized and researched, the detection technology can overcome the limitations of limited detection distance, slow response speed and the like of the traditional sensor, as long as flame appears in a video monitoring area, a flame detection program can immediately identify and output an alarm signal, and the detection is more accurate and rapid. The detection technology based on the video image is one of the most effective technologies for realizing the flame detection of the early fire of the highway tunnel at present.
With the development of traffic engineering construction and traffic industry in China, the mileage of traffic tunnels built in China is rapidly prolonged, and due to the ever-increasing traffic flow and road condition improvement and the complexity of article transportation, the risk probability of fire and tunnel abnormal events of the traffic tunnels is increased, and great threat is caused to traffic facilities and human production activities. The intelligent and information management of the tunnel is more and more important. With the rapid development of internet of things equipment and computer technology, a large amount of data of various conditions of the tunnel is accumulated in the intelligent construction process of the tunnel, and the management of how to effectively screen, clean, store, analyze and the like the data is very important. The method has great guiding significance for the intelligent construction of the tunnel at the present stage and in the future by establishing a data management platform integrating data acquisition, processing, storage, calculation analysis and application display.
Disclosure of Invention
This section is for the purpose of summarizing some aspects of embodiments of the invention and to briefly introduce some preferred embodiments. In this section, as well as in the abstract and the title of the invention of this application, simplifications or omissions may be made to avoid obscuring the purpose of the section, the abstract and the title, and such simplifications or omissions are not intended to limit the scope of the invention.
The present invention has been made in view of the problems occurring in the conventional tunnel flame detection system.
Therefore, the invention aims to provide a tunnel flame detection system based on computer vision and artificial intelligence algorithm, which can create a safe tunnel driving environment, ensure the driving safety of the tunnel and reduce tunnel traffic accidents and casualties.
To solve the above technical problem, according to an aspect of the present invention, the present invention provides the following technical solutions:
tunnel flame detection system based on computer vision and artificial intelligence algorithm, it includes flame detection system and data management platform:
wherein:
the flame detection system comprises an infrared module, an ultraviolet module and a camera module, wherein the infrared module adopts three infrared sensors sensitive to infrared rays, one infrared sensor is used for flame detection, the other two infrared sensors are respectively used for detecting background infrared radiation, the ultraviolet module converts flame narrow spectrum signals into electric pulse signals by using a blind ultraviolet sensor (UV 185-260 nm), then the electric pulse signals are input into a calculation chip for calculation and processing, the camera module is outdoor road monitoring camera hardware, a CNN + LSTM-based structured data space-time model fire detection neural network is operated, the fire situation is rapidly and automatically identified, meanwhile, a section of video or image is automatically recorded and stored, and data is transmitted to a data informatization management platform through one of special lines, wifi and 5G;
the data management platform comprises an internet of things module, a data acquisition module, a calculation analysis module, an analysis result transmission module and an operation state monitoring module, wherein the data acquisition module is electrically connected with the flame detection system, the calculation analysis module is electrically connected with the data acquisition module and used for analyzing and predicting results, the calculation analysis module is electrically connected with the analysis result transmission module, the analysis result transmission module is used for sending the analysis prediction results to the road safety monitoring terminal, and the operation state monitoring module is used for monitoring the operation states of the infrared module, the ultraviolet module and the camera module.
As a preferred embodiment of the tunnel flame detection system based on computer vision and artificial intelligence algorithm of the present invention, wherein: the design of the data management platform system comprises a top layer overall architecture design and a distributed system design, and the data of the existing hardware of a sensing layer are completely accessed, and the added novel hardware is accessed.
As a preferred embodiment of the tunnel flame detection system based on computer vision and artificial intelligence algorithm of the present invention, wherein: and the infrared sensor in the infrared module is a point-type infrared detector.
As a preferred embodiment of the tunnel flame detection system based on computer vision and artificial intelligence algorithm of the present invention, wherein: the infrared sensors in the infrared module adopt a programmable algorithm to check the data proportion and the mutual relation received by the three sensors to confirm the probability of fire occurrence.
As a preferred embodiment of the tunnel flame detection system based on computer vision and artificial intelligence algorithm of the present invention, wherein: the ultraviolet module can realize high-speed response and high-sensitivity detection on close-range weak flame and hydrocarbon combustion flame.
Compared with the prior art, the invention has the beneficial effects that:
(1) the multi-sensor fusion combined flame detection system integrates the fusion of the traditional technology and the modern technology, achieves the purpose that the detection performances of various sensors are mutually compensated, and improves the accuracy and the response speed of alarming;
(2) the deployment is convenient, and the distribution distance is long; the system can be used for local calculation or data transmission in server side/cloud calculation, has moderate price and can replace the existing single-mode detector;
(3) the flame detection and video monitoring are integrated, the video visual images can be pushed, and the video visual images are stored, inquired, corrected and further deeply analyzed and researched.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the present invention will be described in detail with reference to the accompanying drawings and detailed embodiments, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise. Wherein:
FIG. 1 is a schematic view of the structure of the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in detail below.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways than those specifically described herein, and it will be apparent to those of ordinary skill in the art that the present invention may be practiced without departing from the spirit and scope of the present invention, and therefore the present invention is not limited to the specific embodiments disclosed below.
Next, the present invention will be described in detail with reference to the drawings, wherein for convenience of illustration, the cross-sectional view of the device structure is not enlarged partially according to the general scale, and the drawings are only examples, which should not limit the scope of the present invention. In addition, the three-dimensional dimensions of length, width and depth should be included in the actual fabrication.
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Example 1
Tunnel flame detection system based on computer vision and artificial intelligence algorithm, including flame detection system and data management platform:
wherein:
the flame detection system comprises an infrared module, an ultraviolet module and a camera module, wherein the infrared module adopts three infrared sensors sensitive to infrared rays, one infrared sensor is used for flame detection, the other two infrared sensors are respectively used for detecting background infrared radiation, the ultraviolet module converts flame narrow spectrum signals into electric pulse signals by using a blind ultraviolet sensor (UV 185-260 nm), then the electric pulse signals are input into a calculation chip for calculation and processing, the camera module is outdoor road monitoring camera hardware, a CNN + LSTM-based structured data space-time model fire detection neural network is operated, the fire situation is rapidly and automatically identified, meanwhile, a section of video or image is automatically recorded and stored, and data is transmitted to a data informatization management platform through one of special lines, wifi and 5G;
according to the characteristics of the fire scene of the tunnel, the design is carried out by selecting the point type infrared detector, the ultraviolet detector and the visible camera module. The integrated three wavelength detection mode of infrared detector wherein, three wavelength detection passageway are two assistants for one owner, and main passageway working band is 4.0 ~ 4.6um wavelength, and the supplementary passageway working band is 5.1 ~ 6.0um, and the supplementary two channel working band is 0.7 ~ 1.1 um. After the detection signal is amplified and processed by a hardware circuit, the detection signal is simultaneously input into a system processor for digital filtering processing, and logical analysis such as threshold value, flicker, correlation quantity and the like is carried out on the processed data by adopting a flame recognition algorithm to give a judgment result. The ultraviolet detector selects a 180-260 nm solar blind type ultraviolet sensing device, and a flame narrow spectrum signal rail-to-rail acquisition/full pulse analysis (PPW) technology is designed, so that the defect that the traditional detector is easy to interfere is avoided. A slope increasing signal detection technology (PAM) is adopted to monitor a detection scene, so that the stability and the continuous usability of the detector are improved. The visible camera module adopts common outdoor road monitoring camera hardware, wherein the algorithm is a deep learning neural network based on a time-space model of CNN (convolutional neural network) + LSTM (long-short term memory artificial neural network)
The data management platform comprises an internet of things module, a data acquisition module, a calculation analysis module, an analysis result transmission module and an operation state monitoring module, wherein the data acquisition module is electrically connected with the flame detection system, the calculation analysis module is electrically connected with the data acquisition module and used for analyzing and predicting results, the calculation analysis module is electrically connected with the analysis result transmission module, the analysis result transmission module is used for sending the analysis prediction results to the road safety monitoring terminal, and the operation state monitoring module is used for monitoring the operation states of the infrared module, the ultraviolet module and the camera module.
Specifically, the design of the data management platform system comprises a top layer overall architecture design and a distributed system design, and the data of the existing hardware of the sensing layer is completely accessed and the added novel hardware is accessed.
Specifically, the infrared sensor in the infrared module selects a point-type infrared detector.
Specifically, the infrared sensors in the infrared module adopt a programmable algorithm to check the data proportion and the mutual relation received by the three sensors to confirm the probability of fire occurrence.
Specifically, the ultraviolet module can realize high-speed response and high-sensitivity detection on close-range weak flame and hydrocarbon combustion flame.
While the invention has been described above with reference to an embodiment, various modifications may be made and equivalents may be substituted for elements thereof without departing from the scope of the invention. In particular, the various features of the disclosed embodiments of the invention may be used in any combination, provided that no structural conflict exists, and the combinations are not exhaustively described in this specification merely for the sake of brevity and resource conservation. Therefore, it is intended that the invention not be limited to the particular embodiments disclosed, but that the invention will include all embodiments falling within the scope of the appended claims.
Claims (5)
1. Tunnel flame detection system based on computer vision and artificial intelligence algorithm, its characterized in that: the flame detection system comprises a flame detection system and a data management platform:
wherein:
the flame detection system comprises an infrared module, an ultraviolet module and a camera module, wherein the infrared module adopts three infrared sensors sensitive to infrared rays, one infrared sensor is used for flame detection, the other two infrared sensors are respectively used for detecting background infrared radiation, the ultraviolet module converts flame narrow spectrum signals into electric pulse signals by using a blind ultraviolet sensor (UV 185-260 nm), then the electric pulse signals are input into a calculation chip for calculation and processing, the camera module is outdoor road monitoring camera hardware, a CNN + LSTM-based structured data space-time model fire detection neural network is operated, the fire situation is rapidly and automatically identified, meanwhile, a section of video or image is automatically recorded and stored, and data is transmitted to a data informatization management platform through one of special lines, wifi and 5G;
the data management platform comprises an internet of things module, a data acquisition module, a calculation analysis module, an analysis result transmission module and an operation state monitoring module, wherein the data acquisition module is electrically connected with the flame detection system, the calculation analysis module is electrically connected with the data acquisition module and used for analyzing and predicting results, the calculation analysis module is electrically connected with the analysis result transmission module, the analysis result transmission module is used for sending the analysis prediction results to the road safety monitoring terminal, and the operation state monitoring module is used for monitoring the operation states of the infrared module, the ultraviolet module and the camera module.
2. The system of claim 1, wherein the system comprises: the design of the data management platform system comprises a top layer overall architecture design and a distributed system design, and the data of the existing hardware of a sensing layer are completely accessed, and the added novel hardware is accessed.
3. The system of claim 1, wherein the system comprises: and the infrared sensor in the infrared module is a point-type infrared detector.
4. The system of claim 1, wherein the system comprises: the infrared sensors in the infrared module adopt a programmable algorithm to check the data proportion and the mutual relation received by the three sensors to confirm the probability of fire occurrence.
5. The system of claim 1, wherein the system comprises: the ultraviolet module can realize high-speed response and high-sensitivity detection on close-range weak flame and hydrocarbon combustion flame.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114373274A (en) * | 2022-03-22 | 2022-04-19 | 烟台淼盾物联技术有限公司 | Building wisdom fire alarm management system and terminal |
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