Vehicle exhaust gas recognition method and vehicle exhaust gas recognition system

Abstract

The application provides a vehicle exhaust identification method and a vehicle exhaust identification system, and the vehicle exhaust identification method comprises the following steps: acquiring a current exhaust image of a vehicle; determining an image matched with the current exhaust image in a vehicle exhaust sampling image set based on a preset matching rule, and determining a gray value of the current exhaust image based on the matched image and an exhaust sampling image gray value association table; judging whether the vehicle smoke emission is abnormal according to the gray value of the current exhaust image and a preset gray value of the exhaust image, and giving an alarm prompt when the vehicle smoke emission is abnormal. The application compares the gray values of the current exhaust image and the standard exhaust image, determines whether the color of the current exhaust is normal, realizes the judgment of whether the exhaust emission is abnormal, and further realizes the automatic monitoring of the faults of the engine or other parts. When it is judged that the color of the current exhaust is abnormal, an alarm prompt is given, so that the driver can check and maintain the vehicle in time.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle exhaust identification, in particular to a vehicle exhaust identification method and a vehicle exhaust identification system. BACKGROUND

[0002] Under normal circumstances, when the engine is working, the color of the exhaust gas discharged from the exhaust pipe should be colorless and transparent. When abnormal combustion occurs, the situation of smoking will occur. When the engine emits smoke abnormally, white smoke, blue smoke and black smoke will be discharged. Both white smoke and blue smoke are in the form of liquid beads, but their diameters are different, and they show different colors under the refraction of light. The white smoke and blue smoke of the engine contain unburned hydrocarbons (containing fuel and lubricating oil), water vapor and incomplete combustion intermediates (such as oxygen-containing hydrocarbons), except water vapor, which all belong to the category of particulates. During the exhaust process, most of them are adsorbed on solid carbon-based particles and coagulate to form large flocs, which will be white smoke or blue smoke under the refraction of light. Black smoke, also known as soot, is mainly formed by the local high temperature, oxygen deficiency, cracking and dehydrogenation of the engine under high pressure combustion conditions, and is mainly composed of solid micro-particles. During the exhaust process, larger soot particles or flocs are formed, causing the exhaust to emit black smoke. The proportion of soot in engine exhaust particulates is related to the operating state of the engine. Generally, when the engine is running at high load, the particulates are mainly soot, and when the engine is running at low load or idling, the particulates are mainly hydrocarbons. The existing method for judging abnormal engine operation by exhaust gas is to visually observe the engine exhaust port and judge the smoke condition by experience. However, this method is greatly affected by human factors and is prone to errors.

[0003] Therefore, how to design a vehicle exhaust identification method that uses machine vision and does not rely on manual observation has become a problem to be solved at present. SUMMARY

[0004] The present application aims to at least improve one of the technical problems existing in the prior art or related art.

[0005] To this end, one object of the present application is to provide a vehicle exhaust identification method.

[0006] Another object of the present application is to provide a vehicle exhaust identification system.

[0007] In order to achieve the above-mentioned object, the technical solution of the first aspect of the present application provides a vehicle exhaust identification method, comprising: acquiring a current exhaust image of a vehicle; determining an image matched with the current exhaust image in a vehicle exhaust sampling image set based on a preset matching rule, and determining a gray value of the current exhaust image based on the matched image and an exhaust sampling image gray value association table; judging whether the vehicle smoke is abnormal according to the gray value of the current exhaust image and a preset gray value of the exhaust image, and giving an alarm prompt when the vehicle smoke is abnormal.

[0008] According to the vehicle exhaust emission identification method provided by this invention, an image of the exhaust emissions emitted by the vehicle at the current moment is acquired. Then, a matching sample image is found in the vehicle exhaust emission sample image set. Furthermore, based on a grayscale value association table, the grayscale value of the matching sample image can be determined. Further, this grayscale value is compared with a preset grayscale value to determine whether the vehicle exhaust is abnormal, and an alarm can be triggered when abnormal exhaust is detected. Thus, this application can monitor the exhaust emissions of a vehicle engine in real time. When abnormal exhaust occurs, it can promptly detect and alarm the engine user, thereby allowing the malfunctioning engine to be stopped immediately, preventing further damage from continued operation, reducing engine maintenance costs, and saving on engine repair expenses.

[0009] In addition, the vehicle exhaust emission identification method in the above-mentioned technical solution provided by the present invention also has the following additional technical features:

[0010] In the above technical solution, the vehicle exhaust gas identification method also includes determining the color of the current exhaust gas based on the grayscale value of the current exhaust gas image when there is an abnormality in the vehicle exhaust gas; and generating a fault analysis report based on the color of the current exhaust gas and a pre-stored exhaust gas color fault association library.

[0011] This technical solution pre-stores the associated information on exhaust gas color, its corresponding fault location, cause, and solution, forming an exhaust gas color-fault association library. When vehicle exhaust is abnormal, the current exhaust gas color can be determined by analyzing the grayscale value of the real-time exhaust image and its correspondence with color. This allows the library to identify the fault location, cause, and solution for that specific exhaust gas color. Subsequent exhaust gas status assessments can quickly match the exhaust gas color to the corresponding fault location, cause, and solution, generating a fault analysis report for easy viewing. This rapid fault analysis report is available to repair personnel, saving them time and improving efficiency. The entire process requires no human intervention, offering high accuracy and efficiency in identifying fault locations and providing solutions. It provides a basis for fault diagnosis, eliminating the need for blindly relying on experience to determine the cause, significantly improving troubleshooting efficiency. Even non-technical personnel can use this solution to understand engine faults and even perform repairs themselves.

[0012] In the above technical solution, before acquiring the current exhaust gas image of the vehicle, the method further includes acquiring a set of vehicle exhaust gas sampling images and establishing a grayscale value association table for the exhaust gas sampling images.

[0013] In this technical solution, before formally monitoring vehicle exhaust emissions, different exhaust emission images are sampled, and a correlation table between exhaust emission images and grayscale values ​​is established. This allows for subsequent matching of the current exhaust emission image with images in the vehicle exhaust emission sampling image set, and the grayscale value of the current exhaust emission image is determined by the grayscale value of the matched image. This method, through grayscale value comparison, can quickly determine the grayscale value of the current image, improving the efficiency of the vehicle exhaust emission identification method.

[0014] In the above technical solution, the steps of generating a vehicle exhaust sampling image set and establishing an exhaust sampling image grayscale value association table specifically include: acquiring multiple sampling images of vehicle exhaust; processing the multiple sampling images to generate new images; storing the multiple sampling images and the newly generated images to form a vehicle exhaust sampling image set; determining the grayscale value of each image in the vehicle exhaust sampling image set; and associating and storing each image with its corresponding grayscale value to form an exhaust sampling image grayscale value association table.

[0015] In this technical solution, by acquiring sampled images of different vehicle exhaust gases and processing and transforming these images, new images are generated, increasing the number of sampled images. The set of these sampled images and the newly generated images constitutes the vehicle exhaust gas sampled image set, thereby increasing the number of different images in the vehicle exhaust gas sampled image set. This ensures that when matching the exhaust gas image at the current moment with the vehicle exhaust gas sampled image set, the grayscale value of the image will not be unable to be determined due to the inability to find a matching image, thus preventing the entire vehicle exhaust gas recognition method from failing and generating errors. Furthermore, in this solution, after determining the sampled images, the grayscale value of each image in the vehicle exhaust gas sampled image set is determined, and each image is associated with its corresponding grayscale value and stored. This guarantees that the grayscale value can be obtained by matching the exhaust gas image at the current moment with the vehicle exhaust gas sampled image set.

[0016] In the above technical solution, processing multiple exhaust gas sampling images includes performing one or more operations on multiple sampling images in the vehicle exhaust gas sampling image set, such as rotation, reflection transformation, flip transformation, scaling transformation, translation transformation, scale transformation, contrast transformation, denoising transformation, and color transformation.

[0017] In this technical solution, rotation, reflection transformation, flip transformation, scaling transformation, translation transformation, scale transformation, contrast transformation, denoising transformation, and color transformation operations are selectively applied to all images in the vehicle exhaust gas sampling image set to increase the number of images in the vehicle exhaust gas sampling image set. This improves the recognition and generalization capabilities of the neural network. When performing image matching on the exhaust gas image at the current moment in the vehicle exhaust gas sampling image set, the grayscale value of the image will not be unable to be determined due to the inability to match the corresponding image. This ensures the continued operation of the entire vehicle exhaust gas recognition method and provides a guarantee that the implementation of the entire method will not produce errors.

[0018] In the above technical solution, acquiring multiple sampled images of vehicle exhaust includes: acquiring exhaust emission images captured by multiple cameras installed on the vehicle at multiple time points; stitching the exhaust emission images captured by multiple cameras at the same time point according to the shooting angles of the multiple cameras to obtain multiple stereo stitched images at different time points; wherein, each stereo stitched image is a sampled image.

[0019] In this technical solution, multiple cameras are deployed on the vehicle to collect exhaust emission images during image acquisition. These images, captured simultaneously by different cameras, are then stitched together from multiple angles, resulting in a multi-dimensional composite image at different time points. This allows for a multi-angle display of the emitted exhaust gases, reflecting their emission status from various perspectives. Not only can it provide sampling images from multiple angles, but it also allows other cameras to continue operating even if one camera detaches or is damaged. This prevents situations where only one camera is available, and the detachment or damage of one camera would disrupt exhaust emission monitoring, ensuring the normal operation of the vehicle exhaust emission identification method. Generally, different vehicle operating states correspond to different time points; by acquiring sampling images at different time points, vehicle exhaust emission images under different operating states can be obtained.

[0020] Preferably, the stereoscopic stitched image can be 3D stitched or planar stitched, which can stitch together exhaust emission images from different angles in various ways, so that the exhaust emission status from various angles can be observed.

[0021] In the above technical solution, obtaining the current exhaust gas image of the vehicle includes: obtaining exhaust gas emission images captured by multiple cameras installed on the vehicle; stitching the exhaust gas emission images captured by multiple cameras at the same time point according to the shooting angles of the multiple cameras to form a three-dimensional stitched image; and storing the three-dimensional stitched image as the current exhaust gas image.

[0022] This technical solution acquires exhaust emission images captured by multiple cameras mounted on the vehicle, stitches together the images simultaneously captured by each camera to obtain a 3D stitched image, and stores this image as the current exhaust emission image. This allows for multi-angle display of emitted exhaust gases, reflecting the state of exhaust emissions from different perspectives. Not only can it provide sampling images from multiple angles, but it also allows other cameras to continue operating even if one camera detaches or is damaged. This prevents situations where only one camera is available, and the detachment or damage of one camera would prevent normal exhaust emission monitoring, thus ensuring the normal operation of the vehicle exhaust emission recognition method.

[0023] In the above technical solution, before acquiring the current exhaust gas image of the vehicle, the method further includes: determining the spatial coordinate information of the exhaust port in the current exhaust gas image based on the stereo stitched image, judging whether the spatial coordinate information meets the preset position requirements, and issuing an alarm prompt when the spatial coordinate information does not meet the preset position requirements.

[0024] In this technical solution, the spatial coordinates of the exhaust outlet in the current exhaust gas image are determined by 3D stitching, and it is judged whether the exhaust outlet position is in a preset position. When the exhaust outlet position deviates from the preset position, an alarm prompt is issued. The user can then check the exhaust outlet according to the prompt, preventing the exhaust outlet from shifting due to assembly errors or increased vehicle usage time, thus preventing the failure to properly acquire exhaust smoke images.

[0025] In the above technical solution, the vehicle exhaust sampling image set includes images under different environmental parameters. When determining the image that matches the current exhaust image in the vehicle exhaust sampling image set based on the preset matching rules, the current environmental parameters are first obtained, and the environmental matching images are filtered from the vehicle exhaust sampling image set according to the current environmental parameters. Then, the image that matches the current exhaust image is determined from the filtered environmental matching images.

[0026] In this technical solution, since the grayscale of exhaust gas varies under different environments, such as humidity and temperature, the grayscale of exhaust gas is affected. By collecting images under different environmental parameters to form a vehicle exhaust gas sampling image set, the exhaust gas image collected at the current moment will not fail or be incorrectly matched due to different environmental parameters during image matching. Collecting environmental parameters before acquiring the exhaust gas image at the current moment ensures the correctness of subsequent image matching and will not affect subsequent steps due to matching failure or error.

[0027] The second aspect of the present invention provides a vehicle exhaust emission recognition system, including an image acquisition device for acquiring images and / or videos of vehicle exhaust emissions; an alarm device for providing an alarm when there is an abnormality in vehicle exhaust emissions; and a processing unit including a first memory and a processor, wherein the first memory stores a computer program, and the processor executes the computer program to implement the method in any of the above-mentioned technical solutions.

[0028] The vehicle exhaust emission identification system provided by the present invention includes an image acquisition device for acquiring images and / or videos of vehicle exhaust emissions, an alarm device for providing an alarm when abnormal vehicle exhaust emissions are detected, and a processing unit. The processing unit includes a first memory storing a computer program and a processor for executing the computer program to implement the method in any of the above-described technical solutions. Furthermore, since the vehicle exhaust emission identification system of this technical solution is used to implement the vehicle exhaust emission identification method in any of the above-described technical solutions, the vehicle exhaust emission identification system of this solution possesses all the beneficial effects of the vehicle exhaust emission identification method in any of the first aspect of the technical solutions described above, which will not be elaborated further here.

[0029] In the above technical solution, the vehicle exhaust gas recognition system also includes a sensor for monitoring environmental parameters; a second memory for storing a database of exhaust gas color and fault associations; and a third memory for storing a set of vehicle exhaust gas sampling images and a table of grayscale values ​​of exhaust gas sampling images.

[0030] In this technical solution, because the grayscale of exhaust gases varies under different environments—for example, humidity and temperature can affect the grayscale—environmental parameters are collected before image matching of the currently acquired exhaust gas image. This prevents matching failures or errors due to different environmental parameters. Collecting environmental parameters before acquiring the current exhaust gas image ensures the correctness of subsequent image matching and prevents matching failures or errors from affecting subsequent steps. By storing each exhaust gas color of a vehicle and the corresponding fault location, cause, and solution, the system can quickly determine the fault location, cause, and solution for that exhaust gas color when a fault occurs, improving the efficiency of the vehicle exhaust gas identification method. Furthermore, storing a set of vehicle exhaust gas sampling images and a grayscale value association table for these images enhances the accuracy of image matching and improves the efficiency of the vehicle exhaust gas identification method.

[0031] In the above technical solution, preferably, the first memory, the second memory, and the third memory can be different memories or the same memory, that is, they can be different storage units of the first memory.

[0032] Additional aspects and advantages of the invention will become apparent in the following description or may be learned by practice of the invention. Attached Figure Description

[0033] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the description of the embodiments taken in conjunction with the following drawings, in which:

[0034] Figure 1 A schematic flowchart of a vehicle exhaust emission identification method according to an embodiment of the present invention is shown;

[0035] Figure 2 A flowchart illustrating a vehicle exhaust emission identification method according to another embodiment of the present invention is shown;

[0036] Figure 3 A flowchart illustrating another embodiment of the vehicle exhaust emission recognition method of the present invention is shown;

[0037] Figure 4 A flowchart illustrating the vehicle exhaust emission identification method provided in the fourth embodiment of the present invention is shown;

[0038] Figure 5 A schematic diagram of the structure of a vehicle exhaust emission recognition system provided in an embodiment of the present invention is shown;

[0039] Figure 6 A block diagram of a vehicle exhaust emission recognition system provided in another embodiment of the present invention is shown.

[0040] in, Figure 5 and Figure 6 The correspondence between the reference numerals and component names in the attached drawings is as follows:

[0041] 500 Vehicle exhaust emission recognition system, 502 Image acquisition device, 504 Alarm device, 506 Processing unit, 5062 First memory, 5064 Processor, 600 Electronic device, 601 Central processing unit, 602 Read-only memory, 603 Random access memory, 604 Bus, 605 Input / output interface, 606 Input unit, 607 Output unit, 608 Storage unit, 609 Communication unit. Detailed Implementation

[0042] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0043] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0044] The following reference Figures 1 to 6 The present invention describes a vehicle exhaust emission identification method and a vehicle exhaust emission identification system according to some embodiments of the present invention.

[0045] An embodiment of the first aspect of the present invention provides a vehicle exhaust emission identification method, such as... Figure 1 As shown, it includes:

[0046] S12: Obtain the current exhaust image of the vehicle;

[0047] S14: Based on the preset matching rules, determine the image that matches the current exhaust image in the vehicle exhaust sampling image set, and determine the gray value of the current exhaust image based on the matching image and the gray value association table of the exhaust sampling image.

[0048] S16: Determine whether the vehicle's exhaust is abnormal based on the grayscale value of the current exhaust image and the preset grayscale value of the exhaust image, and issue an alarm when there is an abnormality in the vehicle's exhaust.

[0049] According to the vehicle exhaust emission recognition method provided in this embodiment, an image of the exhaust emissions emitted by the vehicle at the current moment can be obtained, i.e., a real-time exhaust emission image. Then, a matching sample image is found in the vehicle exhaust emission sample image set. Furthermore, the grayscale value of the matching sample image is determined based on the grayscale value association table of the exhaust emission sample images. The grayscale value of the obtained sample image is the grayscale value corresponding to the exhaust emission image at the current moment, thus determining the grayscale value of the exhaust emission image at the current moment. This grayscale value can then be compared with the grayscale value of a standard exhaust emission image. If there is a deviation between the grayscale value and the grayscale value of the standard exhaust emission image, it indicates a difference in color between the currently acquired exhaust emission image and the standard exhaust emission image; that is, the color of the currently acquired exhaust emission image is not white. In this case, it can be determined that the vehicle's exhaust emissions are in an abnormal state. Conversely, if the grayscale value is the same as the grayscale value of the standard exhaust emission image, it can be determined that the vehicle's exhaust emissions are normal. This setup compares the grayscale values ​​of the currently collected exhaust gas image with those of a standard exhaust gas image to determine if the current exhaust gas color is normal. This enables the judgment of whether exhaust emissions are abnormal, and thus achieves automatic monitoring of engine or other vehicle malfunctions. When the current exhaust gas color is determined to be non-standard, i.e., not colorless and transparent, an alarm is triggered so that the driver can promptly inspect and repair the vehicle. Simultaneously, because this application monitors engine exhaust anomalies in real time, it promptly detects and alarms the engine user when an anomaly occurs, allowing for immediate shutdown of the faulty engine to prevent further damage and reduce engine repair costs. Without real-time anomaly monitoring, what might have been a minor fault could have resulted in the entire engine being damaged due to delayed detection; what could have been resolved by replacing a small part might have necessitated replacing the entire engine.

[0050] In any of the above embodiments, preferably, it further includes determining the color of the current exhaust gas based on the grayscale value of the current exhaust gas image when there is an abnormality in the vehicle exhaust smoke; and generating a fault analysis report based on the color of the current exhaust gas and a pre-stored exhaust gas color fault association library.

[0051] In these embodiments, the exhaust gas color, its corresponding fault location, cause, and solution are pre-associated and stored, forming an exhaust gas color-fault association library. When vehicle exhaust is abnormal, the current exhaust gas color can be obtained by mapping the grayscale value of the real-time exhaust gas image to the color. This allows the identification of the fault location, cause, and solution corresponding to that exhaust gas color through the association library. Subsequent exhaust gas status assessments can quickly match the exhaust gas color to the corresponding fault location, cause, and solution, generating a fault analysis report for viewing. This rapid fault analysis report is available for maintenance personnel, saving them time and improving efficiency. The entire process requires no human intervention, offering high accuracy and efficiency in identifying the fault location, while also providing solutions. This provides a basis for fault diagnosis, eliminating the need for blindly relying on experience to determine the cause, significantly improving troubleshooting efficiency. Even non-technical personnel can use this solution to understand the engine's fault and even perform repairs themselves.

[0052] Table 1. Fault Locations, Causes, and Solutions for Engines Emitting Black Smoke

[0053]

[0054]

[0055] Table 2: Fault Locations, Causes, and Solutions for Engines Emitting Blue Smoke

[0056]

[0057] Table 3. Fault Locations, Causes, and Solutions for Engines Emitting White Smoke

[0058]

[0059] In any of the above embodiments, preferably, before acquiring the current exhaust image of the vehicle, the method further includes acquiring a set of vehicle exhaust sampling images and establishing an exhaust sampling image grayscale value association table.

[0060] In these embodiments, before formally monitoring vehicle exhaust emissions, different exhaust emission images are sampled, and a correlation table between exhaust emission images and grayscale values ​​is established. This allows for subsequent matching of the current exhaust emission image with images in the vehicle exhaust emission sampling image set, and the grayscale value of the current exhaust emission image can be determined by the grayscale value of the matched image. This method, through grayscale value comparison, can quickly determine the grayscale value of the current image, improving the efficiency of the vehicle exhaust emission identification method.

[0061] See below. Figure 2 This invention introduces another embodiment of a vehicle exhaust emission recognition method, which includes the following steps:

[0062] S21: Acquire multiple sampled images of vehicle exhaust emissions;

[0063] S22: Process multiple sampled images to generate new images, and store the multiple sampled images and the newly generated images to form a vehicle exhaust sampling image set;

[0064] S23: Determine the grayscale value of each image in the vehicle exhaust sampling image set, associate and store each image with its corresponding grayscale value to form an exhaust sampling image grayscale value association table.

[0065] S24: Obtain the current exhaust image of the vehicle;

[0066] S25: Based on preset matching rules, determine the image that matches the current exhaust image in the vehicle exhaust sampling image set, and determine the gray value of the current exhaust image based on the matching image and the gray value association table of the exhaust sampling image.

[0067] S26: Determine whether the vehicle's exhaust is abnormal based on the grayscale value of the current exhaust image and the preset grayscale value of the exhaust image, and issue an alarm when there is an abnormality in the vehicle's exhaust.

[0068] In these embodiments, by acquiring sampled images of different vehicle exhaust gases and processing and transforming these images, new images are generated, increasing the number of sampled images. The set of these sampled images and the newly generated images constitutes the vehicle exhaust gas sampled image set, thereby increasing the number of different images in the vehicle exhaust gas sampled image set. This ensures that when performing image matching on the current exhaust gas image within the vehicle exhaust gas sampled image set, the grayscale value of the image will not be undetermined due to the inability to find a matching image, thus preventing the entire vehicle exhaust gas recognition method from failing and generating errors. Furthermore, in this scheme, after determining the sampled images, the grayscale value of each image in the vehicle exhaust gas sampled image set is determined, and each image is associated with and stored with its corresponding grayscale value. This guarantees that subsequent image matching between the current exhaust gas image and the vehicle exhaust gas sampled image set will yield the correct grayscale value.

[0069] In any of the above embodiments, preferably, processing multiple exhaust gas sampling images includes performing one or more operations on multiple sampling images in the vehicle exhaust gas sampling image set, such as rotation, reflection transformation, flip transformation, scaling transformation, translation transformation, scale transformation, contrast transformation, denoising transformation, and color transformation.

[0070] In these embodiments, rotation, reflection transformation, flip transformation, scaling transformation, translation transformation, scale transformation, contrast transformation, denoising transformation, and color transformation operations are selectively applied to all images in the vehicle exhaust sampling image set to increase the number of images in the vehicle exhaust sampling image set. This improves the recognition and generalization capabilities of the neural network. When performing image matching on the exhaust image at the current moment in the vehicle exhaust sampling image set, the grayscale value of the image will not be unable to be determined due to the inability to match the corresponding image. This ensures the continuation of the entire vehicle exhaust identification method and provides a guarantee that the implementation of the entire method will not produce errors.

[0071] In any of the above embodiments, preferably, such as Figure 3 As shown, S21, which acquires multiple sampled images of vehicle exhaust, specifically includes:

[0072] S212: At multiple time points, acquire exhaust emission images captured by multiple cameras installed on the vehicle;

[0073] S214: Stitch together exhaust emission images captured by multiple cameras at the same time point according to the shooting angles of the multiple cameras to obtain multiple stereo stitched images at different time points.

[0074] In these embodiments, when acquiring sampled images, multiple cameras are deployed on the vehicle to collect exhaust emission images. These images, captured by different cameras simultaneously, are then stitched together from multiple angles, resulting in a multi-dimensional stitched image at different time points. This allows for a multi-angle display of the emitted exhaust gases, reflecting their emission status from different perspectives. Not only can it provide sampled images from multiple angles, but it also allows other cameras to continue operating even if one camera detaches or is damaged. This prevents the inability to perform exhaust emission monitoring if only one camera is available, ensuring the normal operation of the vehicle exhaust emission identification method. Since multiple time points correspond to different vehicle operating states, exhaust emission images of the vehicle under different operating conditions can be acquired.

[0075] In this embodiment, preferably, the stereoscopic stitched image can be 3D stitched or planar stitched, which can stitch together exhaust emission images from different angles in various ways, so that the stitched image of exhaust emission status from various angles can be observed.

[0076] In any of the above embodiments, preferably, such as Figure 4 As shown, step S12, which acquires the current exhaust image of the vehicle, specifically includes:

[0077] S122: Acquire exhaust emission images captured by multiple cameras installed on the vehicle;

[0078] S124: Stitch together exhaust emission images captured by multiple cameras at the same time point according to the shooting angles of the multiple cameras to form a three-dimensional stitched image, and store the three-dimensional stitched image as the current exhaust emission image.

[0079] In these embodiments, exhaust emission images captured by multiple cameras mounted on the vehicle are acquired, and the exhaust emission images captured by each camera simultaneously are stitched together to obtain a stereoscopic image, which is then stored as the current exhaust emission image. This allows for multi-angle display of the emitted exhaust gases, reflecting the state of exhaust emission from different perspectives. Not only can it provide sampling images from multiple angles, but it also allows other cameras to continue operating even if one camera detaches or is damaged. This prevents the inability to perform normal exhaust emission monitoring if there is only one camera, and the detachment or damage of one camera would prevent this, ensuring the normal operation of the vehicle exhaust emission recognition method.

[0080] In any of the above embodiments, preferably, before acquiring the current exhaust gas image of the vehicle, the method further includes: determining the spatial coordinate information of the exhaust port in the current exhaust gas image based on the stereo stitched image, determining whether the spatial coordinate information meets the preset position requirements, and issuing an alarm prompt when the spatial coordinate information does not meet the preset position requirements.

[0081] In these embodiments, the spatial coordinates of the exhaust port in the current exhaust gas image are determined by stereoscopic image stitching, and it is determined whether the exhaust port position is at a preset position. When the exhaust port position deviates from the preset position, an alarm is issued. The user can then check the exhaust port according to the prompt, preventing the exhaust port from shifting due to assembly errors or increased vehicle usage time, thus preventing the proper acquisition of exhaust smoke images and the failure to execute the method of this application.

[0082] In any of the above embodiments, preferably, the vehicle exhaust sampling image set includes images under different environmental parameters. When determining an image that matches the current exhaust image in the vehicle exhaust sampling image set based on a preset matching rule, the current environmental parameters are first obtained, and environmental matching images are filtered from the vehicle exhaust sampling image set according to the current environmental parameters. Then, the image that matches the current exhaust image is determined from the filtered environmental matching images.

[0083] In these embodiments, since the grayscale of exhaust gas varies under different environments, such as humidity and temperature, the grayscale of exhaust gas is affected. By collecting images under different environmental parameters to form a vehicle exhaust gas sampling image set, the exhaust gas image collected at the current moment will not fail or be incorrectly matched due to different environmental parameters during image matching. Collecting environmental parameters before acquiring the exhaust gas image at the current moment ensures the correctness of subsequent image matching and will not affect subsequent steps due to matching failure or error.

[0084] A second aspect of the present invention provides a vehicle exhaust emission recognition system 500, such as... Figure 5 As shown, the vehicle exhaust emission recognition system 500 specifically includes an image acquisition device 502 for acquiring images and / or videos of vehicle exhaust emissions; an alarm device 504 for providing an alarm when there is an abnormality in vehicle exhaust emissions; and a processing unit 506, including a first memory 5062 and a processor 5064. The first memory stores a computer program, and the processor executes the computer program to implement the method in any of the above embodiments.

[0085] The vehicle exhaust emission recognition system 500 provided in this embodiment includes an image acquisition device 502 for acquiring images and / or videos of vehicle exhaust emissions, an alarm device 504 for issuing an alarm when abnormal vehicle exhaust emissions are detected, and a processing unit 506. The processing unit includes a first memory 5062 storing a computer program and a processor 5064 executing the computer program to implement the method in any of the above embodiments. Since the vehicle exhaust emission recognition system 500 in this embodiment is used to implement the vehicle exhaust emission recognition method in any of the above embodiments, it possesses all the beneficial effects of the vehicle exhaust emission recognition method in any of the first aspect embodiments, which will not be elaborated further here.

[0086] In any of the above embodiments, preferably, the vehicle exhaust emission recognition system further includes a sensor for monitoring environmental parameters; a second memory for storing an exhaust emission color-fault association library; and a third memory for storing a set of vehicle exhaust emission sampling images and an exhaust emission sampling image grayscale value association table.

[0087] In these embodiments, because the grayscale of exhaust gases varies under different environments—for example, humidity and temperature can affect the grayscale—environmental parameters are collected before image matching of the currently acquired exhaust gas image. This prevents image matching failures or errors due to different environmental parameters. Collecting environmental parameters before acquiring the current exhaust gas image ensures the correctness of subsequent image matching and prevents matching failures or errors from affecting subsequent steps. By storing each exhaust gas color of a vehicle and the corresponding fault location, cause, and solution, the system can quickly determine the fault location, cause, and solution based on pre-stored information when a fault occurs, improving the efficiency of the vehicle exhaust gas identification method. Storing a set of vehicle exhaust gas sampling images and a grayscale value association table for these images further enhances the accuracy of image matching and improves the efficiency of the vehicle exhaust gas identification method.

[0088] In any of the above embodiments, preferably, the first memory, the second memory, and the third memory can be different memories or the same memory, that is, they can be different storage units of the first memory.

[0089] In this application, the vehicle exhaust emission recognition method can be implemented using electronic devices. See below for details. Figure 6 The following is a detailed description of the electronic device provided by the embodiments of the present invention.

[0090] like Figure 6 As shown, the electronic device 600 provided in this embodiment of the invention includes: a central processing unit 601, which can perform various appropriate actions and processes according to computer program instructions stored in a read-only memory 602 or loaded from a storage unit 608 into a random access memory 603. The random access memory 603 can also store various programs and data required for the operation of the electronic device 600. The central processing unit 601, the read-only memory 602, and the random access memory 603 are interconnected via a bus 604. An input / output interface 605 is also connected to the bus 604. Multiple components in the electronic device 600 are connected to the input / output interface 605, including: an input unit 606, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a disk, optical disk, etc.; and a communication unit 609, such as a network card, modem, wireless transceiver, etc. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks. The central processing unit 601 executes the various methods and processes described above.

[0091] An embodiment of the third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, it implements the steps of the method as described in any of the above embodiments, and thus possesses all the technical effects of the vehicle exhaust emission identification method, which will not be elaborated further here.

[0092] Computer-readable storage media can include any medium capable of storing or transmitting information. Examples of computer-readable storage media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, and so on. Code segments can be downloaded via computer networks such as the Internet and intranets.

[0093] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying vehicle exhaust emissions, characterized in that, include: Acquire the vehicle's current exhaust image; Based on preset matching rules, an image matching the current exhaust image is determined in the vehicle exhaust sampling image set, and the gray value of the current exhaust image is determined based on the matching image and the gray value association table of exhaust sampling images. The system determines whether the vehicle's exhaust emissions are abnormal based on the grayscale value of the current exhaust image and the preset grayscale value of the exhaust image, and issues an alarm when there are abnormalities in the vehicle's exhaust emissions. The vehicle exhaust sampling image set includes images under different environmental parameters, including humidity and temperature. When determining an image that matches the current exhaust image in the vehicle exhaust sampling image set based on a preset matching rule, the current environmental parameters are first obtained, and images that match the environment are filtered from the vehicle exhaust sampling image set according to the current environmental parameters. Then, the image that matches the current exhaust image is determined from the filtered images that match the environment. The vehicle exhaust emission identification method also includes: When there is an abnormality in the vehicle's exhaust emissions, the color of the current exhaust gas is determined based on the grayscale value of the current exhaust gas image; A fault analysis report is generated based on the current exhaust gas color and a pre-stored exhaust gas color fault association library. The fault analysis report includes: the fault location, cause and solution for the engine emitting black smoke, the fault location, cause and solution for the engine emitting blue smoke, and the fault location, cause and solution for the engine emitting white smoke. The acquisition of the vehicle's current exhaust image includes: Acquire exhaust emission images captured by multiple cameras installed on the vehicle; The exhaust emission images captured by multiple cameras at the same time point are stitched together according to the shooting angles of the multiple cameras to form a three-dimensional stitched image, and the three-dimensional stitched image is stored as the current exhaust emission image; Before acquiring the vehicle's current exhaust image, the following steps are also included: Based on the stereoscopic stitched image, the spatial coordinates of the exhaust port in the current exhaust gas image are determined. It is then determined whether the spatial coordinates meet the preset position requirements. If the spatial coordinates do not meet the preset position requirements, an alarm is issued.

2. The vehicle exhaust emission identification method according to claim 1, characterized in that, Before acquiring the vehicle's current exhaust image, the method further includes: Obtain a set of vehicle exhaust gas sampling images and establish a grayscale value association table for the exhaust gas sampling images.

3. The vehicle exhaust emission identification method according to claim 2, characterized in that, The step of acquiring a set of vehicle exhaust gas sampling images and establishing a grayscale value association table for the exhaust gas sampling images specifically includes: Acquire multiple sampled images of vehicle exhaust emissions; The sampled images are processed to generate new images, and the sampled images and the newly generated images are stored to form a vehicle exhaust gas sampling image set. The grayscale value of each image in the vehicle exhaust sampling image set is determined, and each image is associated with its corresponding grayscale value and stored to form an exhaust sampling image grayscale value association table.

4. The vehicle exhaust emission identification method according to claim 3, characterized in that, Processing multiple sampled images includes: Perform one or more of the following operations on multiple exhaust gas sampling images in the vehicle exhaust gas sampling image set: rotation, reflection transformation, flip transformation, scaling transformation, translation transformation, scale transformation, contrast transformation, denoising transformation, and color transformation.

5. The vehicle exhaust emission identification method according to claim 3, characterized in that, The acquisition of multiple sampled images of vehicle exhaust gas includes: At multiple time points, images of exhaust emissions captured by multiple cameras installed on the vehicle are obtained; The exhaust emission images captured by multiple cameras at the same time point are stitched together according to the shooting angles of the multiple cameras to obtain multiple stereo stitched images at different time points. Each of the stereoscopic stitched images is a sampled image.

6. A vehicle exhaust emission recognition system, characterized in that, include: Image acquisition device for acquiring images and / or videos of vehicle exhaust; An alarm device is used to alert the user when there is an abnormality in the vehicle's exhaust emissions. The processing unit includes a first memory and a processor, wherein the first memory stores a computer program, and the processor executes the computer program to implement the vehicle exhaust emission identification method as described in any one of claims 1 to 5.

7. The vehicle exhaust emission identification system according to claim 6, characterized in that, It also includes at least one of the following: Sensors are used to monitor environmental parameters; The second memory is used to store a database linking exhaust gas color to faults. The third memory is used to store the vehicle exhaust gas sampling image set and the grayscale value association table of the exhaust gas sampling images.

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