Carriage state detection method, device, computer equipment and readable storage medium

Abstract

The present application proposes a method, apparatus, computer device, and readable storage medium for detecting the state of a vehicle compartment. The method includes: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, obtaining a vehicle compartment image; determining the state of the compartment lid based on the compartment image; and determining the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or the compartment lid. The present application can obtain the state of the vehicle compartment loading space and the compartment lid based on the laser ranging data and the vehicle compartment image, and obtain the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or the compartment lid, thereby achieving comprehensive detection of the vehicle compartment state and reducing the influence of the external environment during measurement, thereby improving the accuracy and reliability of vehicle compartment state detection.

Description

Technical Field

[0001] The present application relates to the field of data processing technology, and in particular to a vehicle compartment status detection method, device, computer equipment and readable storage medium. Background Art

[0002] With the rapid development of cities, various construction projects are booming, such as the construction of transportation facilities such as subways and overpasses. During the implementation of these projects, large amounts of materials such as slag, sand, and gravel are often transported by transport vehicles. Therefore, it is necessary to inspect the loading status of transport vehicles during transportation for relevant supervision and management.

[0003] However, during the process of conceiving and implementing this application, the inventors discovered at least the following problems: existing detection technologies suffer from low measurement accuracy and reliability, and are easily affected by the working environment. Therefore, a method that can improve the accuracy and reliability of vehicle cabin status measurement is urgently needed.

[0004] The preceding description is intended to provide general background information and does not necessarily constitute prior art. Summary of the Invention

[0005] In response to the above technical problems, the present application provides a vehicle state detection method, device, computer equipment and readable storage device, which can improve the accuracy and reliability of vehicle state detection.

[0006] To solve the above technical problems, the present application provides a method for detecting a carriage state, comprising the following steps:

[0007] Determine whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data;

[0008] If yes, then obtain the carriage image;

[0009] determining a state of a compartment cover according to the compartment image;

[0010] The state of the vehicle compartment is determined based on the state of the vehicle compartment loading space and / or the compartment lid.

[0011] Optionally, determining the state of the compartment lid according to the compartment image includes:

[0012] Extracting compartment cover feature information from the compartment image;

[0013] The state of the compartment cover is determined according to the compartment cover characteristic information.

[0014] Optionally, determining the state of the carriage according to the state of the carriage loading space and / or the carriage cover includes:

[0015] If the state of the compartment lid is the closed state, determining the state of the compartment according to the historical information of the compartment loading space;

[0016] If the state of the compartment lid is an unclosed state, the state of the compartment is determined to be a first preset state.

[0017] Optionally, after determining that the state of the vehicle compartment is a first preset state if the state of the compartment lid is an unclosed state, the method further includes:

[0018] When it is determined through the neural network model that the vehicle compartment image contains a target object, an early warning message is generated.

[0019] Optionally, the method further includes:

[0020] If the current laser ranging data of the vehicle is greater than or equal to a preset threshold, the state of the vehicle compartment is determined to be a second preset state.

[0021] Optionally, determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data includes:

[0022] Performing filtering on the current laser ranging data of the vehicle;

[0023] It is determined whether the carriage loading space changes according to the filtered laser ranging data.

[0024] Optionally, the process of acquiring the neural network model includes:

[0025] Collect image information of the target object and establish a training set of the target object;

[0026] The training set of the target object is trained through a convolutional neural network to obtain a neural network model of the target object.

[0027] Accordingly, an embodiment of the present application further provides a vehicle compartment state detection device, comprising:

[0028] The loading space module is used to determine whether the loading space of the vehicle compartment has changed based on the vehicle's current laser ranging data;

[0029] An image acquisition module, configured to acquire an image of the carriage after determining that the loading space of the carriage has changed;

[0030] A compartment lid status module, configured to determine a compartment lid status based on the compartment image;

[0031] The carriage state module is used to determine the state of the carriage according to the state of the carriage loading space and / or the carriage cover.

[0032] The present application also proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned vehicle state detection methods when executing the computer program.

[0033] The present application also proposes a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned vehicle compartment status detection methods are implemented.

[0034] The implementation of the embodiments of the present application has the following beneficial effects:

[0035] As described above, the present application provides a method, apparatus, computer device, and readable storage medium for detecting a vehicle compartment state, wherein the method includes: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, acquiring a vehicle compartment image; determining the state of the compartment lid based on the compartment image; and determining the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or compartment lid. The present application can obtain the state of the vehicle compartment loading space and compartment lid based on the laser ranging data and the vehicle compartment image, and obtain the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or compartment lid, thereby achieving comprehensive detection of the vehicle compartment state and reducing the influence of the external environment during measurement, thereby improving the accuracy and reliability of vehicle compartment state detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the specification, are used to explain the principles of the present application. In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for describing the embodiments. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without inventive work.

[0037] Figure 1 1 is a flow chart of a method for detecting a carriage state provided in an embodiment of the present application;

[0038] Figure 2 is a schematic diagram of a process for determining a carriage state provided in an embodiment of the present application;

[0039] Figure 3 Schematic diagram of the structure of the carriage state detection device provided in an embodiment of the present application;

[0040] Figure 4 This is a schematic structural diagram of a first embodiment of a computer device provided in an embodiment of the present application;

[0041] Figure 5 It is a structural diagram of the second implementation of the computer device provided in the examples of this application.

[0042] The purpose of this application, its features, and advantages will be further described in conjunction with the embodiments and with reference to the accompanying drawings. The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and the accompanying text are not intended to limit the scope of the present application in any way, but rather to illustrate the concepts of this application to those skilled in the art by reference to specific embodiments. DETAILED DESCRIPTION

[0043] Exemplary embodiments will be described in detail herein, with examples illustrated in the accompanying drawings. In the following description, when referring to the drawings, identical numerals in different figures represent identical or similar elements, unless otherwise indicated. The embodiments described in the following exemplary embodiments are not intended to represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

[0044] It should be noted that, in this document, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, an element defined by the sentence "comprising a ..." does not exclude the presence of other identical elements in the process, method, article or device comprising the element. In addition, components, features, and elements with the same name in different embodiments of the present application may have the same meaning or different meanings, and their specific meanings need to be determined by their explanation in the specific embodiment or further combined with the context of the specific embodiment.

[0045] It should be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish information of the same type from each other. For example, without departing from the scope of this document, first information may also be referred to as second information, and similarly, second information may also be referred to as first information. Depending on the context, the term "if" as used herein may be interpreted as "when," "when," or "in response to a determination." Furthermore, as used herein, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context indicates otherwise. It should be further understood that the terms "comprising" and "including" indicate the presence of the described features, steps, operations, elements, components, items, types, and / or groups, but do not exclude the presence, occurrence, or addition of one or more other features, steps, operations, elements, components, items, types, and / or groups. The terms "or," "and / or," "including at least one of the following," etc., used herein, may be interpreted as inclusive, or mean any one or any combination. For example, “comprising at least one of the following: A, B, C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”; and for another example, “A, B or C” or “A, B and / or C” means “any of the following: A; B; C; A and B; A and C; B and C; A and B and C”. An exception to this definition will occur only when a combination of elements, functions, steps or operations are inherently mutually exclusive in some manner.

[0046] It should be understood that, although the various steps in the flowchart in the embodiment of the present application are shown in sequence according to the indication of the arrows, these steps are not necessarily performed in sequence in the order indicated by the arrows. Unless clearly stated herein, the execution of these steps is not strictly limited in order, and they can be performed in other orders. Moreover, at least a portion of the steps in the figure may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and their execution order is not necessarily performed in sequence, but can be performed in turn or alternately with at least a portion of other steps or sub-steps or stages of other steps.

[0047] As used herein, the words "if" and "if" may be interpreted as "at the time of" or "when" or "in response to determining" or "in response to detecting," depending on the context. Similarly, the phrases "if it is determined" or "if (stated condition or event) is detected" may be interpreted as "when it is determined" or "in response to the determination" or "when detecting (stated condition or event)" or "in response to detecting (stated condition or event)," depending on the context.

[0048] It should be noted that in this article, step codes such as S10 and S20 are used for the purpose of expressing the corresponding content more clearly and concisely, and do not constitute a substantial limitation on the order. When implementing the step, those skilled in the art may execute S20 first and then S10, etc., but these should all be within the scope of protection of this application.

[0049] It should be understood that the specific embodiments described herein are only used to explain the present application and are not intended to limit the present application.

[0050] In the subsequent description, the use of suffixes such as "module", "component" or "unit" to represent elements is only for the purpose of facilitating the description of the present application and has no specific meaning. Therefore, "module", "component" or "unit" can be used interchangeably.

[0051] Currently, the existing carriage status detection technologies are mainly divided into load sensor detection methods and camera detection methods. The former mainly uses the load sensor installed on the vehicle chassis to detect the degree of deformation of the carriage to determine the empty or loaded state of the carriage, while the latter uses the camera's image sensor to dynamically identify the state of the carriage.

[0052] However, the existing technology has some defects. For example, the load sensor detection method has high installation requirements, the installation process is complicated and time-consuming, and it is easy to be damaged due to direct force and harsh working environment during the measurement process, resulting in high costs. In addition, the measurement accuracy is easily affected by external conditions, and the accuracy and stability are poor. The measurement accuracy of the camera detection method is easily affected by changes in the external environment such as light, resulting in poor image imaging effects and thus affecting the accuracy of image recognition. In addition, the image recognition algorithm has high requirements for image quality and is easily affected by the condition of the vehicle compartment, reducing the accuracy of the measurement.

[0053] In order to solve the above problems, the present application proposes a method, device, computer equipment and readable storage medium for detecting the state of the carriage. By detecting laser ranging data and carriage image recognition, the carriage loading space and lid status are comprehensively considered to determine the state of the carriage, thereby reducing the influence of the external environment during measurement, and effectively improving the accuracy and reliability of the carriage state detection.

[0054] See also Figure 1 , Figure 1 : is a flow chart of a method for detecting a carriage state provided by an embodiment of the present application. The method for detecting a carriage state may specifically include:

[0055] S1. Determine whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data.

[0056] Specifically, for step S1, firstly, the current laser ranging data of the vehicle is obtained, and laser data analysis is performed on the laser ranging data to determine whether the loading space of the vehicle compartment has changed.

[0057] Optionally, in some embodiments, step S1 may specifically include:

[0058] S11. Filter the vehicle's current laser ranging data;

[0059] S12. Determine whether the carriage loading space has changed based on the filtered laser ranging data.

[0060] Specifically, the vehicle is equipped with at least one laser sensor, which can acquire laser ranging data. This laser ranging data can be the distance between the laser sensor and the object loaded in the vehicle compartment. After acquiring the laser ranging data, the laser ranging data is filtered, for example, using a Kalman filter. This filtering is not limited here, and other filtering methods can also be applied to this embodiment. After filtering, the filtered laser ranging data is analyzed using an algorithm such as a sliding window method to determine whether the loading space status in the vehicle compartment has changed. Compared to infrared sensors, the laser sensor used in this embodiment is not affected by light, effectively reducing the influence of the external environment during the detection process.

[0061] In a specific embodiment, after obtaining the vehicle laser ranging data, the laser ranging data will be pre-processed, including de-jittering processing, to improve the accuracy of subsequent data analysis of the laser ranging data.

[0062] S2. If yes, obtain the vehicle compartment image.

[0063] Specifically, in step S2, after determining that the vehicle loading space has changed, a vehicle compartment image is captured, including but not limited to capture via an onboard camera. After acquiring the vehicle compartment image, the image is preprocessed, such as by binarization and normalization, to improve the accuracy and efficiency of subsequent image recognition. If it is determined that the vehicle loading space has not changed, the current vehicle compartment state is determined to be the previous state.

[0064] S3. Determine the state of the compartment lid according to the compartment image.

[0065] Specifically, in step S3, the current state of the vehicle compartment lid is determined mainly based on the compartment image obtained in step S2, and it is determined whether the current state of the vehicle compartment lid is a closed state.

[0066] Optionally, in some embodiments, step S3 may specifically include:

[0067] S31 extracts lid feature information from the carriage image;

[0068] S32. Determine the state of the compartment lid according to the compartment lid characteristic information.

[0069] Specifically, when extracting the feature information of the compartment lid from the compartment image, a compartment lid detection algorithm can be used to extract features, thereby classifying the compartment lid state according to the compartment lid feature information, such as empty state, fully loaded state, and closed state. Of course, other feature extraction algorithms can also be used, such as convolutional neural network algorithms. In actual application, since transport vehicles have a variety of compartment lid types, such as iron covers, tarpaulins, double-opening and rocker arms, the image features corresponding to different compartment lid types are also different. Therefore, before extracting the compartment lid feature information from the compartment image, different compartment lid types will also be calibrated to specify the type of the compartment.

[0070] In a specific embodiment, the detection algorithms for the trunk lids of different types of carriages are also different. This embodiment takes the double-opening trunk lid type as an example. For the double-opening trunk lid type, a detection algorithm combining global features and local features is required. The double-opening trunk lid has multiple obvious global features. For example, the features of the vertical projection histogram after binarization are very obvious. By setting weights for each global feature, the score of the global feature can be obtained. The double-opening trunk lid also has local features with obvious texture features. Therefore, this embodiment divides the carriage area into sixteen local areas, uses algorithms such as GLCM (gray level co-occurrence matrix algorithm) to collect texture features of the image, uses 10,000 pre-collected pictures to train the SVM classifier, and finally uses the classifier to perform trunk lid detection in actual use, and finally obtains the score of the local features.

[0071] S4. Determine the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or the compartment lid.

[0072] Specifically, for step S4, after obtaining the current vehicle compartment loading space (i.e., compartment state space situation) and the state of the compartment lid, a comprehensive judgment is made based on the state of the compartment loading space and the compartment lid to ultimately determine the current state of the compartment.

[0073] Optionally, in some embodiments, Figure 2 As shown, step S4 may specifically include:

[0074] S41. If the lid is closed, the state of the compartment is determined based on the historical information about the compartment loading space;

[0075] S42. If the state of the compartment lid is in the non-closed state, determine that the state of the compartment is in the first preset state.

[0076] Specifically, if the lid is detected to be closed, the current state of the carriage is determined by combining the historical carriage loading space information of the onboard state machine. For example, if the historical carriage loading space information at the previous moment indicates a loaded vehicle state, the current state of the carriage is a loaded vehicle with the box closed; if the historical carriage loading space information at the previous moment indicates an empty vehicle state, the current state of the carriage is an empty vehicle with the box closed. If the lid is detected to be open, for example, if the lid is not detected, the carriage is considered to be in the first preset state, where the first preset state is a loaded vehicle with the box open.

[0077] Optionally, in some embodiments, Figure 2 As shown, after step S42, the method may further include:

[0078] S43. When the neural network model determines that the vehicle compartment image contains a target object, a warning message is generated.

[0079] Specifically, after determining that the state of the carriage is the first preset state in step S42, that is, when the state of the carriage is the loaded unpacking state, the system further includes performing image recognition on the carriage image using a preset neural network model. When it is determined that the carriage image contains a target object, a corresponding warning message is generated. The target object can be transport objects such as soil, sand, gravel, and mud. Based on the different similarity percentages obtained for the various transport objects, the object with the highest similarity is selected as the recognition result, and the corresponding warning message is output. Management personnel receive the warning message for real-time supervision and management, thereby preventing the spillage of debris or gravel on urban roads, which not only pollutes the environment but also affects normal urban traffic, and better manages transport vehicles.

[0080] Optionally, in some embodiments, the process of acquiring the neural network model includes:

[0081] Collect image information of the target object and establish a training set of the target object;

[0082] The training set of the target object is trained through a convolutional neural network to obtain a neural network model of the target object.

[0083] In a specific embodiment, before performing image recognition on the carriage image through the neural network model, it also includes pre-building a neural network model, pre-collecting pictures of various different transport objects (stones, sand, mud, etc.), building a convolutional neural network, and after training the model, using the model for reasoning to obtain the similarity percentages corresponding to different types of transport targets. The highest similarity is taken as the recognition result to determine the type of transport object in the carriage.

[0084] Optionally, in some embodiments, the method may further include:

[0085] If the current laser ranging data of the vehicle is greater than or equal to the preset threshold, the state of the vehicle compartment is determined to be the second preset state.

[0086] Specifically, the vehicle compartment state detection method further includes determining that the vehicle compartment state is a second preset state based on the vehicle's current laser ranging data, the second preset state being an empty vehicle state. A threshold for when the vehicle compartment is empty is first calibrated. When the vehicle's laser ranging data is acquired in real time, the laser ranging data is compared with the calibrated threshold. If the laser ranging data is less than the calibrated threshold, the vehicle compartment load space state is determined to be not empty. If the laser ranging data is greater than or equal to the calibrated threshold, the vehicle compartment load space state is determined to be empty.

[0087] As can be seen from the above, the vehicle compartment state detection method provided by the embodiment of the present application includes: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, obtaining a vehicle compartment image; determining the state of the compartment lid based on the vehicle compartment image; and determining the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or compartment lid. It can be seen that the vehicle compartment state detection method of the embodiment of the present application, by collecting laser ranging data and vehicle compartment images from a laser sensor, using a laser algorithm and an image algorithm to complement each other, adds image recognition constraints after fusing the laser ranging data from the laser sensor, thereby comprehensively detecting the state of the vehicle compartment based on the state of the vehicle compartment loading space and / or compartment lid, greatly improving the accuracy of vehicle compartment state recognition. In addition, the laser ranging data can also make up for the shortcoming that image recognition results are easily affected by external environments such as lighting, reducing the problem of low accuracy and efficiency caused by environmental influences during measurement, achieving the effect of accurate recognition in all weather conditions, and further improving the accuracy and reliability of vehicle compartment state detection.

[0088] Correspondingly, this application also provides a vehicle compartment status detection device, please refer to Figure 3 , Figure 3 It is a structural diagram of the vehicle compartment status detection device provided in this application, which may specifically include a loading space module 100, an image acquisition module 200, a lid status module 300 and a vehicle compartment status module 400.

[0089] The loading space module 100 is used to determine whether the loading space of the vehicle compartment has changed based on the current laser ranging data of the vehicle.

[0090] Specifically, the loading space module 100 first obtains the current laser ranging data of the vehicle, and performs laser data analysis on the laser ranging data to determine whether the loading space of the vehicle compartment has changed.

[0091] Optionally, in some embodiments, the loading space module 100 may specifically include:

[0092] A filtering unit, used for filtering the vehicle's current laser ranging data;

[0093] The first determination unit is used to determine whether the loading space of the vehicle compartment has changed according to the laser ranging data after filtering.

[0094] The image acquisition module 200 is used to acquire the vehicle compartment image after determining that the loading space of the vehicle compartment has changed.

[0095] Specifically, after determining that the loading space of the vehicle compartment has changed, the image acquisition module 200 captures the vehicle compartment image, including but not limited to using an onboard camera. After acquiring the vehicle compartment image, the image is pre-processed, such as by binarization and normalization, to improve the accuracy and efficiency of subsequent image recognition.

[0096] The compartment lid status module 300 is used to determine the status of the compartment lid according to the compartment image.

[0097] Specifically, the compartment lid status module 300 mainly determines the current status of the vehicle compartment lid based on the compartment image acquired by the image acquisition module 200, and determines whether the current status of the vehicle compartment lid is a closed state.

[0098] Optionally, in some embodiments, the compartment lid status module 300 may specifically include:

[0099] An extraction unit, used for extracting compartment cover feature information from the compartment image;

[0100] The second determination unit is used to determine the state of the compartment cover according to the compartment cover characteristic information.

[0101] The carriage state module 400 is used to determine the state of the carriage according to the state of the carriage loading space and / or the carriage cover.

[0102] Specifically, for the carriage status module 400, after obtaining the vehicle's current carriage loading space (i.e., the carriage status space situation) and the status of the compartment lid, a comprehensive judgment is made based on the two states to finally determine the current state of the carriage. The carriage status can specifically include the empty vehicle open box state, the empty vehicle closed box state, the heavy vehicle open box state and the heavy vehicle closed box state.

[0103] Optionally, in some embodiments, the carriage state module 400 may specifically include:

[0104] a first state determining unit, configured to determine the state of the carriage according to historical carriage loading space information if the state of the carriage lid is the closed state;

[0105] a second state determining unit, configured to determine that the state of the vehicle compartment is a first preset state if the state of the compartment lid is an unclosed state;

[0106] The early warning unit is used to generate early warning information when it is determined through a neural network model that the vehicle compartment image contains a target object.

[0107] In summary, the vehicle compartment state detection device provided by the embodiment of the present application first determines whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data through the loading space module 100; the image acquisition module 200 acquires the vehicle compartment image after determining that the vehicle compartment loading space has changed; the lid state module 300 determines the lid state based on the vehicle compartment image; and the vehicle compartment state module 400 determines the vehicle compartment state based on the state of the vehicle compartment loading space and / or lid. It can be seen that the vehicle compartment state detection device of the embodiment of the present application, by collecting the laser ranging data and vehicle compartment image from the laser sensor, using the laser algorithm and the image algorithm to complement each other, adds image recognition constraints after fusing the laser ranging data from the laser sensor, thereby comprehensively detecting the vehicle compartment state based on the vehicle compartment loading space and / or lid state, greatly improving the accuracy of vehicle compartment state recognition; in addition, the laser ranging data from the laser sensor can also compensate for the shortcoming that the image recognition result is easily affected by external environment such as lighting, reduce the problem of low accuracy and efficiency caused by environmental influences during measurement, achieve the effect of accurate recognition in all weather conditions, and further improve the accuracy and reliability of vehicle compartment state detection.

[0108] The present application also provides a computer device. Figure 4 , Figure 4 1 is a schematic diagram of the structure of a first embodiment of a computer device provided in an embodiment of the present application. The computer device includes a memory 10 and a processor 20. The memory 10 stores a computer program. When the processor 20 executes the computer program, it implements a method for detecting a vehicle compartment state, including: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, acquiring a vehicle compartment image; determining the state of the compartment lid based on the compartment image; and determining the state of the vehicle compartment based on the state of the compartment loading space and / or the compartment lid.

[0109] The present application also provides a computer device, which may be a server. Figure 5 , Figure 51 is a schematic structural diagram of a second embodiment of a computer device provided in an embodiment of the present application. The computer device includes a processor, a memory, a network interface, and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The database of the computer device is used to store data such as a vehicle state detection method. The network interface of the computer device is used to communicate with an external terminal via a network connection.

[0110] When executed by a processor, the computer program implements a method for detecting a vehicle compartment state. The method includes: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, acquiring a vehicle compartment image; determining the state of the compartment lid based on the compartment image; and determining the state of the vehicle compartment based on the state of the compartment loading space and / or the compartment lid.

[0111] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a method for detecting a vehicle compartment status is implemented, comprising the steps of: determining whether the vehicle compartment loading space has changed based on the vehicle's current laser ranging data; if so, obtaining a vehicle compartment image; determining the status of the compartment lid based on the vehicle compartment image; and determining the status of the vehicle compartment based on the status of the compartment loading space and / or compartment lid.

[0112] The above-mentioned carriage state detection method, the embodiment of the present application collects the laser ranging data and carriage images of the laser sensor, uses the laser algorithm and the image algorithm to complement each other, and adds the image recognition constraint conditions after fusing the laser ranging data of the laser sensor, so as to comprehensively detect the carriage state according to the carriage loading space and / or the lid state, thereby greatly improving the accuracy of the carriage state recognition; in addition, the laser sensor data can also make up for the shortcomings of the image recognition results that are easily affected by the external environment such as light, reduce the problem of low accuracy and efficiency caused by environmental influences during measurement, achieve the effect of accurate recognition around the clock, and further improve the accuracy and reliability of the carriage state detection.

[0113] It is understood that the above scenarios are merely examples and do not limit the application scenarios of the technical solutions provided in the embodiments of this application. The technical solutions of this application can also be applied to other scenarios. For example, those skilled in the art will appreciate that with the evolution of system architecture and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application will also be applicable to similar technical problems.

[0114] The serial numbers of the above-mentioned embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0115] The steps in the method of the embodiment of the present application can be adjusted in order, combined and deleted according to actual needs.

[0116] The units in the device of the embodiment of the present application can be merged, divided and deleted according to actual needs.

[0117] In this application, the same or similar terminology, technical solutions and / or application scenario descriptions are generally only described in detail the first time they appear. When they appear again later, they are generally not repeated for the sake of brevity. When understanding the technical solutions and other contents of this application, for the same or similar terminology, technical solutions and / or application scenario descriptions that are not described in detail later, you can refer to the previous relevant detailed descriptions.

[0118] In this application, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0119] The various technical features of the technical solution of this application can be combined arbitrarily. In order to make the description concise, not all possible combinations of the various technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this application.

[0120] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus the necessary general hardware platform, and of course can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) as mentioned above, and includes a number of instructions for enabling a terminal device (which can be a mobile phone, computer, server, controlled terminal, or network device, etc.) to execute the method of each embodiment of the present application.

[0121] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When software is used for implementation, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function according to the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (e.g., coaxial cable, optical fiber, digital subscriber line) or wireless (e.g., infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more available media integrations. The available medium can be a magnetic medium (e.g., a floppy disk, a storage disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state storage disk Solid State Disk (SSD)).

[0122] The above are only preferred embodiments of the present application and do not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for detecting a carriage state, characterized in that: The steps include: Filter the vehicle's current laser ranging data; Analyzing the filtered laser ranging data using a sliding window algorithm to determine whether the carriage loading space has changed; If yes, then obtain the carriage image; Extracting compartment cover feature information from the compartment image; determining the state of the compartment cover according to the compartment cover characteristic information; If the current laser ranging data of the vehicle is greater than or equal to a preset threshold, determining that the state of the vehicle compartment is a second preset state; If the state of the compartment lid is the closed state, determining the state of the compartment according to the historical information of the compartment loading space; If the state of the compartment lid is an unclosed state, the state of the compartment is determined to be a first preset state, and when it is determined through a neural network model that the compartment image contains a target object, a warning message is generated.

2. The vehicle compartment state detection method according to claim 1, characterized in that: The acquisition process of the neural network model includes: Collect image information of the target object and establish a training set of the target object; The training set of the target object is trained through a convolutional neural network to obtain a neural network model of the target object.

3. A carriage state detection device, characterized in that: include: Loading space module, used to filter the vehicle's current laser ranging data; Analyzing the filtered laser ranging data using a sliding window algorithm to determine whether the carriage loading space has changed; An image acquisition module, configured to acquire an image of the carriage after determining that the loading space of the carriage has changed; A compartment lid status module, configured to determine a compartment lid status based on the compartment image; A carriage state module, configured to extract carriage cover feature information from the carriage image; determining the state of the compartment cover according to the compartment cover characteristic information; If the current laser ranging data of the vehicle is greater than or equal to a preset threshold, determining that the state of the vehicle compartment is a second preset state; If the state of the compartment lid is closed, the state of the compartment is determined based on the historical information of the compartment loading space; if the state of the compartment lid is not closed, the state of the compartment is determined to be a first preset state, and when it is determined through a neural network model that the target object is contained in the compartment image, a warning message is generated.

4. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the vehicle compartment state detection method according to claim 1 or 2 are implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the vehicle compartment state detection method according to claim 1 or 2 are implemented.

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