Street light fault detection methods, devices, electronic equipment, and storage media
By acquiring nighttime road images from vehicles and using light source detection algorithms to determine street light malfunctions, and combining this with vehicle travel trajectories, the images are sent to the cloud for fusion processing. This solves the problems of low efficiency and poor accuracy in existing street light detection methods, and achieves efficient and accurate street light malfunction detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- ZHIDAO NETWORK TECH (BEIJING) CO LTD
- Filing Date
- 2022-11-15
- Publication Date
- 2026-06-30
AI Technical Summary
Existing street light detection methods are inefficient, inaccurate, and costly. Manual inspection is prone to fatigue, and image-based detection solutions are prone to false alarms, increasing manpower and material costs.
By acquiring nighttime road images from the vehicle, using light source detection algorithms to determine street light locations and detect faults, and combining these images with vehicle travel trajectories, the data is sent to the cloud for fusion processing, thereby improving detection accuracy and efficiency.
It improved the accuracy and efficiency of street light fault detection, reduced the false detection rate, and reduced the waste of human and material resources.
Smart Images
Figure CN115761670B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of street light detection technology, and in particular to a street light fault detection method, device, electronic equipment, and storage medium. Background Technology
[0002] Streetlights provide strong protection for road safety, but they can also malfunction or break down after long-term use. It often takes a long time from the onset of a malfunction to repair and restoration, mainly because the malfunctions are not detected in time. Therefore, the detection of streetlight malfunctions is particularly important.
[0003] Existing street light inspection methods mainly rely on manual nighttime checks, which suffer from drawbacks such as low efficiency, operator fatigue, and high costs. Some image-based detection solutions for identifying faulty street lights are prone to inaccurate detection and false alarms, further increasing labor and material costs. Summary of the Invention
[0004] This application provides a street light fault detection method, device, electronic equipment, and storage medium to improve the accuracy and efficiency of street light fault detection.
[0005] The embodiments of this application adopt the following technical solutions:
[0006] In a first aspect, embodiments of this application provide a street light fault detection method, the method being executed by a vehicle, wherein the method includes:
[0007] Acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images;
[0008] A preset light source detection algorithm is used to detect the light source positions of streetlights in the nighttime road image, and the light source detection results of the streetlight positions are obtained.
[0009] The fault detection result of the street light at the vehicle end is determined based on the light source detection result at the street light location;
[0010] The vehicle's driving trajectory corresponding to the street light location is obtained, and the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location are sent to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location.
[0011] Optionally, acquiring a nighttime road image of the current road segment and determining the location of streetlights in the nighttime road image includes:
[0012] Acquire high-precision map data of the current road segment and the calibration relationship between the high-precision map data and camera images;
[0013] Based on the calibration relationship between the high-precision map data and the camera image, the street light positions in the high-precision map data are projected onto the nighttime road image to obtain the street light positions in the nighttime road image.
[0014] Optionally, the light source detection result at the streetlight location is the light source detection result of multiple consecutive frames, and determining the streetlight fault detection result at the vehicle end based on the light source detection result at the streetlight location includes:
[0015] If the light source detection results for multiple consecutive frames are all that no light source is detected at the street light location, then it is determined that there is a faulty street light at the street light location.
[0016] Otherwise, it is determined that there is no faulty street light at the street light location.
[0017] Optionally, obtaining the vehicle's driving trajectory corresponding to the streetlight location and sending the streetlight fault detection result from the vehicle and the vehicle's driving trajectory corresponding to the streetlight location to the cloud includes:
[0018] If the street light fault detection result at the vehicle end indicates that there is a faulty street light at the street light location, then the vehicle's driving trajectory within a preset range corresponding to the street light location is obtained.
[0019] The vehicle sends the street light fault detection results and the vehicle's driving trajectory within a preset range corresponding to the street light location to the cloud.
[0020] Secondly, embodiments of this application also provide a street light fault detection method, the method being executed by the cloud, wherein the method includes:
[0021] Acquire street light fault detection data reported by multiple vehicle terminals, the street light fault detection data including the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location;
[0022] The street light fault detection results from each vehicle terminal are merged based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0023] The street light fault detection results in the cloud are determined based on the fusion processing results;
[0024] The street light fault detection data is obtained based on any of the methods described above.
[0025] Optionally, the process of fusing the streetlight fault detection results from each vehicle terminal based on the vehicle driving trajectory corresponding to the streetlight location reported by each vehicle terminal includes:
[0026] The degree of overlap between the driving trajectories of multiple vehicles is determined based on the vehicle driving trajectories corresponding to the street light locations reported by each vehicle terminal.
[0027] Based on the degree of overlap in the driving trajectories of multiple vehicles, multiple target street light fault detection results are determined, wherein the target street light fault detection results are multiple street light fault detection results at the same street light location;
[0028] Multiple target street light fault detection results are fused together to determine the street light fault detection results in the cloud based on the fusion processing results.
[0029] Thirdly, embodiments of this application also provide a street light fault detection device, the device being applied to a vehicle, wherein the device includes:
[0030] The first acquisition unit is used to acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images;
[0031] The detection unit is used to detect the light source positions of streetlights in the nighttime road image using a preset light source detection algorithm, and obtain the light source detection results of the streetlight positions.
[0032] The first determining unit is used to determine the street light fault detection result at the vehicle end based on the light source detection result at the street light location;
[0033] The sending unit is used to obtain the vehicle driving trajectory corresponding to the street light location, and send the street light fault detection result from the vehicle end and the vehicle driving trajectory corresponding to the street light location to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result from the vehicle end and the vehicle driving trajectory corresponding to the street light location.
[0034] Fourthly, embodiments of this application also provide a street light fault detection device, the device being applied in a cloud environment, wherein the device includes:
[0035] The second acquisition unit is used to acquire street light fault detection data reported by multiple vehicle terminals. The street light fault detection data includes the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location.
[0036] The fusion unit is used to fuse the street light fault detection results from each vehicle based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0037] The second determining unit is used to determine the street light fault detection results in the cloud based on the fusion processing results;
[0038] The street light fault detection data is obtained based on the aforementioned device.
[0039] Fifthly, embodiments of this application also provide a street light fault detection system, the system including a vehicle-side terminal and a cloud-based terminal, the vehicle-side terminal including the aforementioned street light fault detection device, and the cloud-based terminal including the aforementioned other street light fault detection device.
[0040] Sixthly, embodiments of this application also provide an electronic device, including:
[0041] Processor; and
[0042] A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform any of the methods described above.
[0043] In a seventh aspect, embodiments of this application also provide a computer-readable storage medium that stores one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform any of the methods described above.
[0044] The above-mentioned technical solutions adopted in the embodiments of this application can achieve the following beneficial effects: The street light fault detection method of the embodiments of this application is executed by the vehicle end. First, a nighttime road image of the current road segment is acquired and the street light position in the nighttime road image is determined; then, a preset light source detection algorithm is used to perform light source detection on the street light position in the nighttime road image to obtain the light source detection result of the street light position; then, the street light fault detection result of the vehicle end is determined based on the light source detection result of the street light position; finally, the vehicle driving trajectory corresponding to the street light position is acquired, and the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light position are sent to the cloud, so that the cloud determines the street light fault detection result of the cloud based on the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light position. The street light fault detection method of the embodiments of this application detects street light faults from the perspective of the vehicle end. Through continuous detection and data accumulation by multiple vehicle ends, the efficiency and accuracy of street light fault detection are improved. Furthermore, the vehicle driving trajectory data provided by the vehicle end provides a basis for subsequent fusion of street light fault detection results from multiple vehicle ends, further improving the accuracy of street light fault detection. Attached Figure Description
[0045] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:
[0046] Figure 1 This is a flowchart illustrating a street light fault detection method according to an embodiment of this application;
[0047] Figure 2This is a schematic diagram of the structure of a street light fault detection device according to an embodiment of this application;
[0048] Figure 3 This is a flowchart illustrating another street light fault detection method in an embodiment of this application;
[0049] Figure 4 This is a schematic diagram of another street light fault detection device in the embodiments of this application;
[0050] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation
[0051] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0052] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.
[0053] This application provides a street light fault detection method, which is executed by a vehicle. Figure 1 The diagram shows a flowchart of a street light fault detection method according to an embodiment of this application. The method includes at least the following steps S110 to S140:
[0054] Step S110: Obtain a nighttime road image of the current road segment and determine the location of streetlights in the nighttime road image.
[0055] The street light fault detection method of this application embodiment can be executed by any vehicle equipped with an OBU (Onboard Unit), which collects nighttime road images of the current road segment through an onboard camera and determines the location of street lights in the road images.
[0056] Step S120: Use a preset light source detection algorithm to detect the light source positions of streetlights in the nighttime road image, and obtain the light source detection results of the streetlight positions.
[0057] After determining the location of streetlights in a nighttime road image, further light source detection algorithms are needed to detect the presence of light sources at those locations. Pre-set light source detection algorithms could include pixel intensity judgment based on image filtering or light source detection models trained using deep learning. Of course, those skilled in the art can flexibly choose the appropriate method for light source detection based on existing technologies, and no specific limitations are imposed here.
[0058] Step S130: Determine the street light fault detection result at the vehicle end based on the light source detection result at the street light location.
[0059] After obtaining the light source detection results at the street light location, it can be determined whether there is a light source at the street light location. If a light source is detected at the street light location, it means that the street light is in normal working condition. If no light source is detected at the street light location, it means that the street light is in a faulty state. This is how the street light fault detection results at the vehicle end are obtained.
[0060] Step S140: Obtain the vehicle driving trajectory corresponding to the street light location, and send the street light fault detection result from the vehicle end and the vehicle driving trajectory corresponding to the street light location to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result from the vehicle end and the vehicle driving trajectory corresponding to the street light location.
[0061] Since each vehicle-mounted unit equipped with an OBU can be used to detect street light faults, multiple vehicle-mounted unit street light fault detection results can be obtained. Therefore, for the same street light location, multiple vehicle-mounted unit street light fault detection results will also be obtained. Thus, this application embodiment can obtain corresponding street light fault detection results from multiple different vehicle-mounted unit dimensions by using vehicle-mounted units to detect street light faults. Finally, the results are sent to the cloud for fusion processing, which improves the accuracy of street light fault detection results through crowdsourcing.
[0062] To enable subsequent fusion processing, when reporting street light fault detection results, the vehicle needs to provide the location of the street light corresponding to the detected fault. However, since the vehicle's positioning may have some errors, in order to ensure the accuracy of cloud fusion, the vehicle can further report its local driving trajectory near each street light location. The driving trajectory can compensate for the impact of the vehicle's positioning error on the accuracy of the street light fault detection results.
[0063] The street light fault detection method of this application detects street light faults from the perspective of the vehicle. Through continuous detection and data accumulation from multiple vehicle terminals, the efficiency and accuracy of street light fault detection are improved. Furthermore, the vehicle driving trajectory data provided by the vehicle terminals provides a basis for subsequent fusion of street light fault detection results from multiple vehicle terminals, further improving the accuracy of street light fault detection.
[0064] In some embodiments of this application, obtaining a nighttime road image of the current road segment and determining the street light positions in the nighttime road image includes: obtaining high-precision map data of the current road segment and the calibration relationship between the high-precision map data and the camera image; based on the calibration relationship between the high-precision map data and the camera image, projecting the street light positions in the high-precision map data onto the nighttime road image to obtain the street light positions in the nighttime road image.
[0065] In determining the location of streetlights in a nighttime road image, this embodiment of the application first acquires high-precision map data of the current road segment and the calibration relationship between the high-precision map data and the camera image. The high-precision map data not only contains high-precision coordinates but also accurate road shapes, including the slope, curvature, heading, elevation, and lateral tilt data for each lane. Furthermore, the type of markings on each lane, the color of lane lines, the road median strip, streetlights, and arrows and text on road signs are also presented in the high-precision map. Therefore, the streetlight information for the current road segment can be determined based on the high-precision map data.
[0066] Since the high-precision map provides the absolute position of the streetlights on the current road segment, i.e., the position of the streetlights in the world coordinate system, the position of the streetlights in the world coordinate system is transformed into the camera image coordinate system based on the pre-calibrated high-precision map data and the relationship between the changes in the camera image and the image data, thereby determining the position of the streetlights in the nighttime road image.
[0067] Even without high-precision map data, streetlight targets in images can be detected using a pre-trained streetlight detection model.
[0068] In some embodiments of this application, the light source detection result at the street light location is the light source detection result of multiple consecutive frames. The step of determining the street light fault detection result at the vehicle end based on the light source detection result at the street light location includes: if the light source detection results of multiple consecutive frames are all that no light source is detected at the street light location, then it is determined that there is a faulty street light at the street light location; otherwise, it is determined that there is no faulty street light at the street light location.
[0069] To ensure the accuracy of street light fault detection results from each vehicle, the vehicle can perform continuous multi-frame detection on the same street light, thereby outputting multi-frame street light fault detection results for the same street light. It should be noted that although the vehicle is in motion, causing the street light's position in the image to change, based on the output frequency of the onboard camera, the same street light will typically still appear continuously in multiple frames of road images, thus allowing for the generation of multi-frame street light fault detection results for the same street light.
[0070] If no light source is detected consecutively at the location where the streetlight's image is projected during vehicle travel, the streetlight is considered faulty, and the location information of the faulty streetlight, the detection results, and the corresponding vehicle trajectory are sent to the cloud. Conversely, if no light source is detected for several consecutive frames, the streetlight cannot be directly considered faulty, and the vehicle does not need to report information. Although the detection results of multiple consecutive frames from a single vehicle may still contain false positives, combining the streetlight fault detection results from multiple vehicles can significantly reduce the impact of false positives.
[0071] In some embodiments of this application, obtaining the vehicle's driving trajectory corresponding to the streetlight location and sending the streetlight fault detection result from the vehicle and the vehicle's driving trajectory corresponding to the streetlight location to the cloud includes: if the streetlight fault detection result from the vehicle indicates that there is a faulty streetlight at the streetlight location, then obtaining the vehicle's driving trajectory within a preset range corresponding to the streetlight location; and sending the streetlight fault detection result from the vehicle and the vehicle's driving trajectory within the preset range corresponding to the streetlight location to the cloud.
[0072] When sending street light fault detection results and corresponding vehicle driving trajectories to the cloud, it is possible to first determine whether there is a faulty street light. If there is a faulty street light, the location of the faulty street light, the fault detection results, and the vehicle's driving trajectory near that street light location are sent to the cloud.
[0073] This application embodiment also provides a street light fault detection device 200, which is applied to a vehicle end, such as... Figure 2 As shown, a schematic diagram of a street light fault detection device according to an embodiment of this application is provided. The device 200 includes: a first acquisition unit 210, a detection unit 220, a first determination unit 230, and a sending unit 240, wherein:
[0074] The first acquisition unit 210 is used to acquire a nighttime road image of the current road segment and determine the location of streetlights in the nighttime road image;
[0075] The detection unit 220 is used to detect the light source position of the street lamp in the night road image using a preset light source detection algorithm, and obtain the light source detection result of the street lamp position;
[0076] The first determining unit 230 is used to determine the street light fault detection result at the vehicle end based on the light source detection result of the street light position;
[0077] The sending unit 240 is used to obtain the vehicle driving trajectory corresponding to the street light location, and send the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light location to the cloud, so that the cloud can determine the street light fault detection result of the cloud based on the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light location.
[0078] In some embodiments of this application, the first acquisition unit 210 is specifically used to: acquire high-precision map data of the current road segment and the calibration relationship between the high-precision map data and the camera image; based on the calibration relationship between the high-precision map data and the camera image, project the street light positions in the high-precision map data onto the nighttime road image to obtain the street light positions in the nighttime road image.
[0079] In some embodiments of this application, the light source detection result of the street lamp location is the light source detection result of multiple consecutive frames. The first determining unit 230 is specifically used to: if the light source detection results of multiple consecutive frames are all that no light source is detected at the street lamp location, then determine that there is a faulty street lamp at the street lamp location; otherwise, determine that there is no faulty street lamp at the street lamp location.
[0080] In some embodiments of this application, the sending unit 240 is specifically used to: if the street light fault detection result of the vehicle end indicates that there is a faulty street light at the street light location, then obtain the driving trajectory of the vehicle within a preset range corresponding to the street light location; and send the street light fault detection result of the vehicle end and the driving trajectory of the vehicle within the preset range corresponding to the street light location to the cloud.
[0081] It is understood that the above-mentioned street light fault detection device can realize all the steps of the street light fault detection method executed by the vehicle end provided in the foregoing embodiments. The relevant explanations of the street light fault detection method are applicable to the street light fault detection device, and will not be repeated here.
[0082] This application also provides another method for detecting street light faults, which is executed by the cloud, such as... Figure 3 The diagram shows a flowchart of another street light fault detection method according to an embodiment of this application. The method includes at least the following steps S310 to S330:
[0083] Step S310: Obtain street light fault detection data reported by multiple vehicle terminals. The street light fault detection data includes the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location.
[0084] The street light fault detection method in this application embodiment is executed by the cloud. When the cloud performs street light fault detection, it needs to first obtain street light fault detection data reported by multiple vehicle terminals. Specifically, it can include the street light fault detection results of each vehicle terminal and the vehicle driving trajectory corresponding to the street light location.
[0085] Step S320: The street light fault detection results from each vehicle terminal are fused based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0086] The vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal reflects which street light location the reported street light fault detection result corresponds to. Therefore, the street light fault detection results from multiple vehicle terminals of the same street light can be fused based on the vehicle driving trajectory data, thereby reducing the impact of the vehicle's own positioning error on the detection results and improving the accuracy of the street light fault detection results.
[0087] Step S330: Determine the street light fault detection results in the cloud based on the fusion processing results; wherein the street light fault detection data is obtained based on the aforementioned method.
[0088] The fusion processing result reflects the fusion of street light fault detection results from multiple vehicle terminals corresponding to each street light location. Therefore, if multiple vehicle terminals detect street light faults multiple times consecutively at the same street light location, the street light can be confirmed as faulty. Otherwise, it cannot be confirmed as faulty. This ensures the accuracy of street light fault detection and reduces the waste of human and material resources caused by false detections.
[0089] In some embodiments of this application, the process of fusing the streetlight fault detection results of each vehicle terminal based on the vehicle driving trajectory corresponding to the streetlight location reported by each vehicle terminal includes: determining the degree of overlap between the driving trajectories of multiple vehicles based on the vehicle driving trajectory corresponding to the streetlight location reported by each vehicle terminal; determining multiple target streetlight fault detection results based on the degree of overlap between the driving trajectories of multiple vehicles, wherein the target streetlight fault detection results are multiple streetlight fault detection results at the same streetlight location; and fusing the multiple target streetlight fault detection results to determine the streetlight fault detection result in the cloud based on the fusion processing result.
[0090] When fusing the street light fault detection results from various vehicle terminals, it is necessary to consider the impact of the vehicle's own positioning error on the detection results. Therefore, the degree of overlap between the vehicle driving trajectories corresponding to the street light locations reported by each vehicle terminal can be calculated. When the degree of overlap is greater than a certain threshold, it can be considered that the street light detection results from these two vehicle terminals correspond to the same street light. In this way, it is possible to determine which vehicle terminal street light detection results correspond to which street lights. Finally, the final cloud-based street light fault detection result is determined based on the street light detection results from multiple vehicle terminals corresponding to each street light. For example, if the street light detection results from multiple vehicle terminals corresponding to a street light all indicate that the street light is faulty, it can be confirmed that the street light is faulty, and relevant personnel can be notified to carry out timely repairs.
[0091] This application embodiment also provides a street light fault detection device 400, which is applied to the cloud, such as... Figure 4 As shown, a schematic diagram of another street light fault detection device in this application embodiment is provided. The device 400 includes: a second acquisition unit 410, a fusion unit 420, and a second determination unit 430, wherein:
[0092] The second acquisition unit 410 is used to acquire street light fault detection data reported by multiple vehicle terminals. The street light fault detection data includes the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location.
[0093] The fusion unit 420 is used to fuse the street light fault detection results of each vehicle terminal according to the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0094] The second determining unit 430 is used to determine the street light fault detection result in the cloud based on the fusion processing result; wherein the street light fault detection data is obtained based on the aforementioned device.
[0095] In some embodiments of this application, the fusion unit 420 is specifically used to: determine the degree of overlap between the driving trajectories of multiple vehicles based on the vehicle driving trajectories corresponding to the street light locations reported by each vehicle terminal; determine multiple target street light fault detection results based on the degree of overlap between the driving trajectories of multiple vehicles, wherein the target street light fault detection results are multiple street light fault detection results at the same street light location; and perform fusion processing on the multiple target street light fault detection results to determine the street light fault detection results in the cloud based on the fusion processing results.
[0096] It is understood that the above-mentioned street light fault detection device can realize all the steps of the street light fault detection method executed by the cloud in the aforementioned embodiments. The relevant explanations of the street light fault detection method are applicable to the street light fault detection device, and will not be repeated here.
[0097] This application also provides a street light fault detection system, which includes a vehicle-mounted terminal and a cloud-based terminal. The vehicle-mounted terminal includes one of the aforementioned street light fault detection devices, and the cloud-based terminal includes another of the aforementioned street light fault detection devices.
[0098] Figure 5 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Please refer to it. Figure 5 At the hardware level, the electronic device includes a processor, and optionally also includes an internal bus, a network interface, and memory. The memory may include main memory, such as high-speed random-access memory (RAM), or non-volatile memory, such as at least one disk drive. Of course, the electronic device may also include other hardware required for other business operations.
[0099] The processor, network interface, and memory can be interconnected via an internal bus, which can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. This bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 5 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.
[0100] Memory is used to store programs. Specifically, programs may include program code, which includes computer operation instructions. Memory may include main memory and non-volatile memory, and provides instructions and data to the processor.
[0101] The processor reads the corresponding computer program from non-volatile memory into main memory and then runs it, forming a street light fault detection device at the logical level. The processor executes the program stored in memory and specifically performs the following operations:
[0102] Acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images;
[0103] A preset light source detection algorithm is used to detect the light source positions of streetlights in the nighttime road image, and the light source detection results of the streetlight positions are obtained.
[0104] The fault detection result of the street light at the vehicle end is determined based on the light source detection result at the street light location;
[0105] The vehicle's driving trajectory corresponding to the street light location is obtained, and the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location are sent to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location.
[0106] Alternatively, it can be used to execute:
[0107] Acquire street light fault detection data reported by multiple vehicle terminals, the street light fault detection data including the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location;
[0108] The street light fault detection results from each vehicle terminal are merged based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0109] The street light fault detection results in the cloud are determined based on the fusion processing results;
[0110] The street light fault detection data is obtained based on any of the methods described above.
[0111] The above is as stated in this application. Figure 1 and Figure 3The method executed by the streetlight fault detection device disclosed in the illustrated embodiment can be applied to a processor or implemented by a processor. The processor may be an integrated circuit chip with signal processing capabilities. During implementation, each step of the above method can be completed by integrated logic circuits in the processor's hardware or by instructions in software form. The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of this application can be directly embodied in the execution of a hardware decoding processor, or executed by a combination of hardware and software modules in the decoding processor. The software module can reside in a mature storage medium in the field, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory, and the processor reads information from the memory and, in conjunction with its hardware, completes the steps of the above method.
[0112] The electronic device can also perform Figure 1 and Figure 3 The method for implementing a street light fault detection device, and the realization of the street light fault detection device in... Figure 1 and Figure 3 The functions of the embodiments shown are not described in detail here.
[0113] This application also proposes a computer-readable storage medium that stores one or more programs, the programs including instructions that, when executed by an electronic device including multiple applications, enable the electronic device to perform... Figure 1 and Figure 3 The method executed by the street light fault detection device in the illustrated embodiment is specifically used to perform the following:
[0114] Acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images;
[0115] A preset light source detection algorithm is used to detect the light source positions of streetlights in the nighttime road image, and the light source detection results of the streetlight positions are obtained.
[0116] The fault detection result of the street light at the vehicle end is determined based on the light source detection result at the street light location;
[0117] The vehicle's driving trajectory corresponding to the street light location is obtained, and the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location are sent to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location.
[0118] Alternatively, it can be used to execute:
[0119] Acquire street light fault detection data reported by multiple vehicle terminals, the street light fault detection data including the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location;
[0120] The street light fault detection results from each vehicle terminal are merged based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal.
[0121] The street light fault detection results in the cloud are determined based on the fusion processing results;
[0122] The street light fault detection data is obtained based on any of the methods described above.
[0123] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0124] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0125] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0126] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0127] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0128] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0129] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0130] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.
[0131] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0132] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.
Claims
1. A street light fault detection method, wherein the method is executed by a vehicle, wherein, The method includes: Acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images; A preset light source detection algorithm is used to detect the light source positions of streetlights in the nighttime road image, and the light source detection results of the streetlight positions are obtained. The fault detection result of the street light at the vehicle end is determined based on the light source detection result at the street light location; The vehicle's driving trajectory corresponding to the street light location is obtained, and the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location are sent to the cloud, so that the cloud can determine the street light fault detection result based on the street light fault detection result on the vehicle end and the vehicle's driving trajectory corresponding to the street light location. The step of obtaining the vehicle driving trajectory corresponding to the street light location and sending the street light fault detection result from the vehicle and the vehicle driving trajectory corresponding to the street light location to the cloud includes: If the street light fault detection result at the vehicle end indicates that there is a faulty street light at the street light location, then the vehicle's driving trajectory within a preset range corresponding to the street light location is obtained. The vehicle sends the street light fault detection results and the vehicle's driving trajectory within a preset range corresponding to the street light location to the cloud.
2. The method as described in claim 1, wherein, The process of acquiring a nighttime road image of the current road segment and determining the location of streetlights in the nighttime road image includes: Acquire high-precision map data of the current road segment and the calibration relationship between the high-precision map data and camera images; Based on the calibration relationship between the high-precision map data and the camera image, the street light positions in the high-precision map data are projected onto the nighttime road image to obtain the street light positions in the nighttime road image.
3. The method as described in claim 1, wherein, The light source detection results at the street light location are the light source detection results of multiple consecutive frames. Determining the street light fault detection results at the vehicle end based on the light source detection results at the street light location includes: If the light source detection results for multiple consecutive frames are all that no light source is detected at the street light location, then it is determined that there is a faulty street light at the street light location. Otherwise, it is determined that there is no faulty street light at the street light location.
4. A street light fault detection method, wherein the method is executed by the cloud, wherein, The method includes: Acquire street light fault detection data reported by multiple vehicle terminals, the street light fault detection data including the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location; The street light fault detection results from each vehicle terminal are merged based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal. The street light fault detection results in the cloud are determined based on the fusion processing results; The street light fault detection data is obtained based on the method described in any one of claims 1 to 3.
5. The method as described in claim 4, wherein, The process of fusing the street light fault detection results from each vehicle terminal based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal includes: The degree of overlap between the driving trajectories of multiple vehicles is determined based on the vehicle driving trajectories corresponding to the street light locations reported by each vehicle terminal. Based on the degree of overlap in the driving trajectories of multiple vehicles, multiple target street light fault detection results are determined, wherein the target street light fault detection results are multiple street light fault detection results at the same street light location; Multiple target street light fault detection results are fused together to determine the street light fault detection results in the cloud based on the fusion processing results.
6. A street light fault detection device, wherein the device is applied to a vehicle end, wherein, The device includes: The first acquisition unit is used to acquire nighttime road images of the current road segment and determine the location of streetlights in the nighttime road images; The detection unit is used to detect the light source positions of streetlights in the nighttime road image using a preset light source detection algorithm, and obtain the light source detection results of the streetlight positions. The first determining unit is used to determine the street light fault detection result at the vehicle end based on the light source detection result at the street light location; The sending unit is used to obtain the vehicle driving trajectory corresponding to the street light location, and send the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light location to the cloud, so that the cloud can determine the street light fault detection result of the cloud based on the street light fault detection result of the vehicle end and the vehicle driving trajectory corresponding to the street light location. The sending unit is specifically used for: If the street light fault detection result at the vehicle end indicates that there is a faulty street light at the street light location, then the vehicle's driving trajectory within a preset range corresponding to the street light location is obtained. The vehicle sends the street light fault detection results and the vehicle's driving trajectory within a preset range corresponding to the street light location to the cloud.
7. A street light fault detection device, wherein the device is applied in a cloud environment, wherein... The device includes: The second acquisition unit is used to acquire street light fault detection data reported by multiple vehicle terminals. The street light fault detection data includes the street light fault detection results from the vehicle terminals and the vehicle driving trajectory corresponding to the street light location. The fusion unit is used to fuse the street light fault detection results from each vehicle based on the vehicle driving trajectory corresponding to the street light location reported by each vehicle terminal. The second determining unit is used to determine the street light fault detection results in the cloud based on the fusion processing results; The street light fault detection data is obtained based on the device described in claim 6.
8. A street light fault detection system, the system comprising a vehicle-mounted terminal and a cloud-based terminal, the vehicle-mounted terminal comprising the device of claim 6, and the cloud-based terminal comprising the device of claim 7.
9. An electronic device, comprising: processor; as well as A memory configured to store computer-executable instructions, which, when executed, cause the processor to perform the method of any one of claims 1 to 3, or to perform the method of any one of claims 4 to 5.
10. A computer-readable storage medium storing one or more programs, which, when executed by an electronic device including a plurality of applications, cause the electronic device to perform the method of any one of claims 1 to 3, or to perform the method of any one of claims 4 to 5.