CN116152686A - Truck tire fire prediction method and system based on unmanned aerial vehicle remote sensing image - Google Patents
Truck tire fire prediction method and system based on unmanned aerial vehicle remote sensing image Download PDFInfo
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Abstract
The invention relates to the technical field of intelligent traffic, in particular to a truck tire fire prediction method and system based on unmanned aerial vehicle remote sensing images. A truck tire fire prediction method based on unmanned aerial vehicle remote sensing images comprises the following steps: the unmanned aerial vehicle acquires data to obtain a visible light image and an infrared image; obtaining tire position information according to the visible light image and the infrared image; obtaining the highest temperature of the tire according to the infrared image and the tire position information; judging according to the tire maximum temperature and a preset temperature threshold value, and calculating according to the tire maximum temperature when the tire maximum temperature is greater than or equal to the preset threshold value to obtain tire ignition time; and when the highest temperature of the tire is smaller than a preset threshold value, the unmanned aerial vehicle continues to monitor. The invention relates to a truck tire fire prediction method with rapid response and accurate prediction.
Description
Technical Field
The invention relates to the technical field of intelligent traffic, in particular to a truck tire fire prediction method and system based on unmanned aerial vehicle remote sensing images.
Background
Along with the crossing development of expressway networks in China, expressway cargo transportation becomes an important component of the material transportation industry. A large number of trucks transport materials between different provinces and different markets, and ensure the normal life of people. As the number of trucks running on roads is increased, the fire accident of truck tires often occurs, which threatens the life and property safety of people. The tire ignition of the truck is different from the ignition of the truck engine or the truck electronic circuit, the tire ignition is not easy to extinguish, the cargo ignition is easy to be caused, and the final economic loss is generally larger.
Current wheel temperature research is mainly conducted around quality detection of wheel manufacturing and internal temperature detection of tires of the automobile itself. In quality inspection of wheel manufacturing, a tire is generally photographed by a thermal infrared imager, and the temperature distribution of the tire is observed. This method is effective for simple stationary background tire distribution and is not suitable for rapid running vehicle tire temperature measurement. The research on the measurement of the internal temperature of the wheel is to utilize a thermocouple to realize the real-time measurement of the internal temperature of the wheel according to the principle of an embedded system and transmit the information of the internal temperature of the wheel to an internal computer of an automobile for processing, thereby monitoring the internal temperature of the wheel in real time. Although such a tire detection method can achieve the temperature of the automobile tire, automobile manufacturers do not install such sensors on ordinary vehicle tires other than luxury vehicles in order to reduce the cost.
Therefore, a truck tire fire prediction method with rapid response and accurate prediction is lacking in the prior art.
Disclosure of Invention
The embodiment of the invention provides a truck tire fire prediction method and system based on unmanned aerial vehicle remote sensing images. The technical scheme is as follows:
in one aspect, a method for predicting a truck tire fire based on an unmanned aerial vehicle remote sensing image is provided, the method is implemented by electronic equipment, and the method comprises the following steps:
s1, acquiring data by an unmanned aerial vehicle to obtain a visible light image and an infrared image;
s2, obtaining tire position information according to the visible light image and the infrared image;
s3, obtaining the highest temperature of the tire according to the infrared image and the tire position information;
s4, judging according to the highest temperature of the tire and a preset temperature threshold value, and executing S5 when the highest temperature of the tire is greater than or equal to the preset threshold value; returning to the step S1 when the highest temperature of the tire is smaller than a preset threshold value;
and S5, calculating according to the highest temperature of the tire to obtain the tire ignition time.
Optionally, the obtaining tire position information according to the visible light image and the infrared image includes:
according to the infrared image, the visible light image is subjected to size adjustment, and a processed visible light image is obtained;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
Wherein the target tire information includes tire anchor frame information and tire relative coordinates.
Optionally, the calculating according to the highest temperature of the tire to obtain the tire ignition time comprises the following steps:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
Optionally, after the highest temperature of the tire is greater than or equal to a preset threshold, the method further comprises:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and sending the license plate information and the unmanned aerial vehicle position information to a traffic management department.
On the other hand, a truck tire fire prediction system based on unmanned aerial vehicle remote sensing images is provided, the system is applied to a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images, the system comprises an unmanned aerial vehicle and electronic equipment, wherein:
the unmanned aerial vehicle is used for collecting data and obtaining visible light images and infrared images;
the electronic equipment is used for obtaining tire position information according to the visible light image and the infrared image; obtaining the highest temperature of the tire according to the infrared image and the tire position information; judging according to the tire maximum temperature and a preset temperature threshold value, and calculating according to the tire maximum temperature when the tire maximum temperature is greater than or equal to the preset threshold value to obtain tire ignition time; and when the highest temperature of the tire is smaller than a preset threshold value, acquiring data by using the unmanned aerial vehicle.
Optionally, the electronic device is further configured to:
according to the infrared image, the visible light image is subjected to size adjustment, and a processed visible light image is obtained;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
Wherein the target tire information includes tire anchor frame information and tire relative coordinates.
Optionally, the electronic device is further configured to:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
Optionally, the system is further configured to:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and sending the license plate information and the unmanned aerial vehicle position information to a traffic management department.
In another aspect, an electronic device is provided, the electronic device including a processor and a memory, the memory storing at least one instruction, the at least one instruction being loaded and executed by the processor to implement the above-described truck tire fire prediction method based on unmanned aerial vehicle remote sensing images.
In another aspect, a computer readable storage medium having stored therein at least one instruction loaded and executed by a processor to implement a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images as described above is provided.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the invention provides a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images, which combines the basic characteristics of visible light and infrared unmanned aerial vehicle remote sensing images, utilizes a deep learning algorithm of a Yolo-tiny architecture to process the visible light images to detect a road truck, and determines the relative positions of wheels; and detecting the highest temperature of the wheel through the infrared image, and predicting the time for the wheel to fire according to the temperature rising speed. The invention monitors the temperature of the tire in real time, predicts the ignition time of the tire, transfers the ignition prediction cost born by truck manufacturers and consumers to social public transportation investors, and indirectly reduces the burden of the consumers. The invention relates to a truck tire fire prediction method with rapid response and accurate prediction.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a truck tire fire prediction method based on an unmanned aerial vehicle remote sensing image, which is provided by an embodiment of the invention;
fig. 2 is a block diagram of a truck tire fire prediction system based on an unmanned aerial vehicle remote sensing image, which is provided by the embodiment of the invention;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the technical problems, technical solutions and advantages to be solved more apparent, the following detailed description will be given with reference to the accompanying drawings and specific embodiments.
The embodiment of the invention provides a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images, which can be realized by electronic equipment, wherein the electronic equipment can be a terminal or a server. The process flow of the method for predicting the train tire fire based on the unmanned aerial vehicle remote sensing image as shown in fig. 1 can comprise the following steps:
s1, acquiring data by an unmanned aerial vehicle to obtain a visible light image and an infrared image.
In a feasible implementation mode, the unmanned aerial vehicle is based on a built-in flight control module, an image acquisition module and a data processing module, and achieves an image acquisition function. The flight control module is used for controlling the flight attitude, the flight speed and the flight direction of the unmanned aerial vehicle; the image acquisition module is used for acquiring side visible light and infrared image data information of a vehicle running on a road in real time; the data processing module is used for processing the image information acquired by the unmanned aerial vehicle, controlling the unmanned aerial vehicle, sending an instruction to the acousto-optic prompt module according to the image processing result, and giving the vehicle information acquired from the image to the information transmission module.
In the whole wagon wheel fire prediction process, the visible light image information and the infrared image information have complementary effects, the thermal infrared image can reflect the temperature information of an object, the temperature of the object can be measured in a contactless manner, but the temperature is affected by an automobile engine and tail gas in a complex environment, the precision of determining the position of the wagon wheel is reduced, and the visible light image can accurately provide the position information of the wagon wheel, but the temperature of the wagon wheel cannot be reflected.
The system provided by the invention collects visible light infrared images through the visible light and infrared module, processes the collected image information in the information processing module, discovers a truck in the driving process according to the visible light images, determines the position of the truck tire, and completes the fire prediction of the truck tire according to the temperature information and the temperature lifting speed of the infrared images.
And S2, obtaining tire position information according to the visible light image and the infrared image.
Optionally, obtaining the tire position information according to the visible light image and the infrared image includes:
according to the infrared image, the size of the visible light image is adjusted to obtain a processed visible light image;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
In a possible embodiment, the position of the truck tire is determined by using the visible light image in the process of acquiring the position information of the truck tire. In order to normally collect visible light images at night, a light supplementing lamp is arranged in the unmanned aerial vehicle image collecting module, the light supplementing lamp is in a closed state in daytime, and a road is illuminated after the light supplementing lamp is turned on at night.
Obtaining a visible light image through an image acquisition module; and adjusting the size of the visible light image according to the size of the infrared image, so as to ensure that the size of the visible light image is consistent with the size of the infrared image. Processing the visible light image by utilizing a YOLO tiny target recognition algorithm based on deep learning, and finding out trucks and truck tires in the image; acquiring the relative coordinates of the tire in the image through the anchor frame coordinate information; and determining the position information of the truck tire in the infrared image according to the target tire information obtained in the visible light image.
Wherein the target tire information includes tire anchor frame information and tire relative coordinates.
In a possible implementation mode, the invention acquires the relative coordinates of the tire in the image through the anchor frame coordinate information; and according to the tire relative coordinates and the tire anchor frame information obtained in the visible light image.
And S3, obtaining the highest temperature of the tire according to the infrared image and the tire position information.
In a possible implementation mode, according to the invention, an infrared image of the side face of the truck is obtained according to the infrared image data; determining position information of a wagon wheel in the image according to the side infrared image of the wagon; and finding the position of the truck tire in the infrared image to obtain the highest temperature of the truck tire.
S4, judging according to the highest temperature of the tire and a preset temperature threshold value, and executing S5 when the highest temperature of the tire is greater than or equal to the preset threshold value; and when the highest temperature of the tire is smaller than the preset threshold value, returning to the step S1.
In a feasible implementation mode, according to a preset temperature threshold, whether the highest temperature of the wagon wheel exceeds a specified normal temperature range is judged, and if the temperature of the wagon wheel is within the normal temperature range, the unmanned aerial vehicle continues patrol aerial photography; if the temperature of the wagon wheel exceeds the specified normal temperature range, the wagon wheel is judged to be too high, the wagon is started to be tracked, and the subsequent ignition time prediction is performed.
And S5, calculating according to the highest temperature of the tire to obtain the tire ignition time.
Optionally, calculating according to the highest temperature of the tire to obtain the tire ignition time, including:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
In a possible embodiment, in the invention, in the prediction of the ignition of the freight car wheel, whether the wheel information is too high is judged according to the temperature information of the infrared image, then the temperature rise speed of the tire is estimated by measuring the temperature of the tire twice every 10 seconds, and finally the ignition time of the tire is predicted by utilizing the temperature rise speed of the tire.
When the tire temperature is determined to be too high, the temperature measured at this time is the highest temperature of the first round, the highest temperature of the truck is measured for the second time after tracking the truck for 10 seconds, and the temperature rise speed of the truck wheel is calculated, and the calculation formula is shown as the following formula (1):
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the temperature rising speed of the tyre>
For the second temperature measurement time of the wagon wheel, +.>
For the first temperature measurement time of the wagon wheel, +.>
Maximum temperature for the second turn of the wagon wheel, +.>
The highest temperature for the first turn of the truck wheel.
After the temperature rising speed of the truck tire is measured, the firing time of the truck wheel can be predicted, and the calculation formula of the time required for firing is shown as the following formula (2):
wherein, the liquid crystal display device comprises a liquid crystal display device,
for the ignition temperature of the tire>
For the temperature rising speed of truck tires, < > for>
Measuring the temperature for the second wagon wheel, +.>
The time required for the truck tire to fire.
When the tire of the truck is required to be on fireTime of interest
After that, the tire ignition time can be predicted, and the prediction formula is as shown in the following formula (3):
wherein, the liquid crystal display device comprises a liquid crystal display device,
time of ignition of truck tire->
For the second temperature measurement time of the wagon wheel, +.>
The time required for the truck tire to fire.
The method has the advantages that the truck based on the visible light image and the tire position of the truck are determined, whether the truck tire is overheated or not is judged based on the tire temperature measurement of the infrared image, the tire temperature rising speed is calculated, the train tire firing time link is predicted, the train tire firing prediction can be realized, and a foundation is laid for avoiding life and property loss caused by train tire firing.
Optionally, after the tire maximum temperature is greater than or equal to the preset threshold, the method further comprises:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and transmitting the license plate information and the unmanned aerial vehicle position information to a traffic management department.
In a feasible implementation mode, the unmanned aerial vehicle is internally provided with the position locating module, the acousto-optic prompting module and the information transmission module to realize the functions of alarming and information sending. When the highest temperature of the tire reaches a preset alarm temperature, a warning is sent to a driver, and when the highest temperature of the tire does not reach the alarm temperature, the unmanned aerial vehicle continues to carry out inspection.
The geographic position positioning module is used for acquiring current longitude and latitude information of the unmanned aerial vehicle by utilizing GPS positioning. The sound-light prompt module of the truck driver has the functions of giving sound-light prompt to the truck driver when the information processing module finds that the temperature of the truck tire is too high, and giving the predicted information of the truck tire on fire through a horn. The information transmission module is used for transmitting truck license plate information of overheat tires, prediction information of fire of the tires and longitude and latitude position information of the unmanned aerial vehicle to the traffic control management department.
The invention provides a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images, which combines the basic characteristics of visible light and infrared unmanned aerial vehicle remote sensing images, utilizes a deep learning algorithm of a Yolo-tiny architecture to process the visible light images to detect a road truck, and determines the relative positions of wheels; and detecting the highest temperature of the wheel through the infrared image, and predicting the time for the wheel to fire according to the temperature rising speed. The invention monitors the temperature of the tire in real time, predicts the ignition time of the tire, transfers the ignition prediction cost born by truck manufacturers and consumers to social public transportation investors, and indirectly reduces the burden of the consumers. The invention relates to a truck tire fire prediction method with rapid response and accurate prediction.
Fig. 2 is a block diagram illustrating a truck tire fire prediction system based on unmanned aerial vehicle remote sensing images, according to an exemplary embodiment. Referring to fig. 2, the system comprises a drone and an electronic device, wherein:
the unmanned aerial vehicle 210 is used for acquiring data and obtaining a visible light image and an infrared image;
an electronic device 220 for obtaining tire position information from the visible light image and the infrared image; obtaining the highest temperature of the tire according to the infrared image and the tire position information; judging according to the highest temperature of the tire and a preset temperature threshold value, and calculating according to the highest temperature of the tire when the highest temperature of the tire is greater than or equal to the preset threshold value to obtain the tire ignition time; and when the highest temperature of the tire is smaller than a preset threshold value, acquiring data by using the unmanned aerial vehicle.
Optionally, the electronic device 220 is further configured to:
according to the infrared image, the size of the visible light image is adjusted to obtain a processed visible light image;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
Wherein the target tire information includes tire anchor frame information and tire relative coordinates.
Optionally, the electronic device 220 is further configured to:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
Optionally, the system is further configured to:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and transmitting the license plate information and the unmanned aerial vehicle position information to a traffic management department.
The invention provides a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images, which combines the basic characteristics of visible light and infrared unmanned aerial vehicle remote sensing images, utilizes a deep learning algorithm of a Yolo-tiny architecture to process the visible light images to detect a road truck, and determines the relative positions of wheels; and detecting the highest temperature of the wheel through the infrared image, and predicting the time for the wheel to fire according to the temperature rising speed. The invention monitors the temperature of the tire in real time, predicts the ignition time of the tire, transfers the ignition prediction cost born by truck manufacturers and consumers to social public transportation investors, and indirectly reduces the burden of the consumers. The invention relates to a truck tire fire prediction method with rapid response and accurate prediction.
Fig. 3 is a schematic structural diagram of an electronic device 300 according to an embodiment of the present invention, where the electronic device 300 may have a relatively large difference due to different configurations or performances, and may include one or more processors (central processing units, CPU) 301 and one or more memories 302, where at least one instruction is stored in the memories 302, and the at least one instruction is loaded and executed by the processors 301 to implement the steps of the method for predicting a truck tire fire based on an unmanned aerial vehicle remote sensing image.
In an exemplary embodiment, a computer readable storage medium, such as a memory comprising instructions executable by a processor in a terminal to perform a truck tire fire prediction method based on unmanned aerial vehicle remote sensing images as described above, is also provided. For example, the computer readable storage medium may be ROM, random Access Memory (RAM), CD-ROM, magnetic tape, floppy disk, optical data storage device, etc.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method for predicting truck tire fires based on unmanned aerial vehicle remote sensing images, the method comprising:
s1, acquiring data by an unmanned aerial vehicle to obtain a visible light image and an infrared image;
s2, obtaining tire position information according to the visible light image and the infrared image;
s3, obtaining the highest temperature of the tire according to the infrared image and the tire position information;
s4, judging according to the highest temperature of the tire and a preset temperature threshold value, and executing S5 when the highest temperature of the tire is greater than or equal to the preset threshold value; returning to the step S1 when the highest temperature of the tire is smaller than a preset threshold value;
and S5, calculating according to the highest temperature of the tire to obtain the tire ignition time.
2. The method for predicting truck tire fire based on unmanned aerial vehicle remote sensing images according to claim 1, wherein the obtaining tire position information based on the visible light image and the infrared image comprises:
according to the infrared image, the visible light image is subjected to size adjustment, and a processed visible light image is obtained;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
3. The method for predicting truck tire fire based on unmanned aerial vehicle remote sensing images according to claim 2, wherein the target tire information comprises tire anchor frame information and tire relative coordinates.
4. The method for predicting the tire firing of the truck based on the unmanned aerial vehicle remote sensing image according to claim 1, wherein the calculating according to the highest temperature of the tire to obtain the tire firing time comprises the following steps:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
5. The method for predicting truck tire fire based on unmanned aerial vehicle remote sensing images according to claim 1, wherein after the tire maximum temperature is greater than or equal to a preset threshold, the method further comprises:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and sending the license plate information and the unmanned aerial vehicle position information to a traffic management department.
6. The utility model provides a freight train tire prediction system that fires based on unmanned aerial vehicle remote sensing image, its characterized in that, the system is used for realizing a freight train tire prediction method that fires based on unmanned aerial vehicle remote sensing image, a freight train tire prediction system that fires based on unmanned aerial vehicle remote sensing image includes unmanned aerial vehicle and electronic equipment, wherein:
the unmanned aerial vehicle is used for collecting data and obtaining visible light images and infrared images;
the electronic equipment is used for obtaining tire position information according to the visible light image and the infrared image; obtaining the highest temperature of the tire according to the infrared image and the tire position information; judging according to the tire maximum temperature and a preset temperature threshold value, and calculating according to the tire maximum temperature when the tire maximum temperature is greater than or equal to the preset threshold value to obtain tire ignition time; and when the highest temperature of the tire is smaller than a preset threshold value, acquiring data by using the unmanned aerial vehicle.
7. The unmanned aerial vehicle remote sensing image-based truck tire fire prediction system of claim 6, wherein the electronic device is further configured to:
according to the infrared image, the visible light image is subjected to size adjustment, and a processed visible light image is obtained;
identifying the processed visible light image through a preset YOLO tiny target identification algorithm to obtain target tire information;
tire position information is obtained based on the target tire information and the infrared image.
8. The unmanned aerial vehicle remote sensing image based truck tire fire prediction system of claim 7, wherein the target tire information comprises tire anchor frame information and tire relative coordinates.
9. The unmanned aerial vehicle remote sensing image-based truck tire fire prediction system of claim 6, wherein the electronic device is further configured to:
the highest temperature of the tire is the highest temperature of the first round, and the recorded measurement time is the first temperature measurement time;
performing secondary temperature measurement on the tire to obtain a secondary highest temperature and a secondary temperature measurement time;
calculating according to the first-round highest temperature, the second-round highest temperature, the first temperature measurement time and the second temperature measurement time to obtain the temperature rising speed of the tire;
and calculating according to the preset tire ignition temperature, the highest temperature of the second round and the tire heating speed to obtain the tire ignition time.
10. The unmanned aerial vehicle remote sensing image based truck tire fire prediction system of claim 6, further comprising:
when the highest temperature of the tire is greater than or equal to a preset threshold value, giving an audible and visual alarm to a driver;
when the driver responds to the audible and visual alarm, the audible and visual alarm is released;
when the driver does not respond to the audible and visual alarm, obtaining license plate information according to the visible light image; the unmanned aerial vehicle performs positioning operation to obtain unmanned aerial vehicle position information;
and sending the license plate information and the unmanned aerial vehicle position information to a traffic management department.
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