CN117809234A

CN117809234A - Security detection method and device, equipment and storage medium

Info

Publication number: CN117809234A
Application number: CN202311536318.7A
Authority: CN
Inventors: 吴晓松; 吴奇文; 罗安杰; 冯惠仪
Original assignee: Guangzhou Keii Electro Optics Technology Co ltd
Current assignee: Guangzhou Keii Electro Optics Technology Co ltd
Priority date: 2023-11-16
Filing date: 2023-11-16
Publication date: 2024-04-02

Abstract

The embodiment of the application discloses a security detection method, a security detection device, security detection equipment and a storage medium, wherein the security detection method comprises the following steps: determining the position of a dangerous chemical vehicle, wherein the dangerous chemical vehicle comprises a vehicle for storing dangerous chemicals, acquiring the temperature of each object in a target area comprising the position of the dangerous chemical vehicle, acquiring an image corresponding to the target area when the temperature of any object exceeds a preset safety temperature, extracting the characteristics of the image corresponding to the target area to obtain image characteristics, analyzing the image characteristics to obtain a safety detection result, wherein the safety detection result comprises abnormal conditions and normal conditions. The method has the advantages that the position information of the dangerous chemical vehicle in the target area can be known in real time, the potential abnormal situation is identified, corresponding measures are timely taken to handle the abnormal situation, the safety of the target area is guaranteed, the safety monitoring capability of the dangerous chemical vehicle in the target area is improved, accident occurrence is prevented, and the safety of personnel and property is protected.

Description

Security detection method and device, equipment and storage medium

Technical Field

The embodiments of the present application relate to security prevention technology, and relate to, but are not limited to, a security detection method, a security detection device, a security detection apparatus, and a storage medium.

Background

In a daily parking scene, passenger and cargo mixed parking is often carried out when a vehicle is parked, and when a vehicle for storing dangerous goods is parked in a parking area, high temperature conditions may be generated around the vehicle, for example, smoke is drawn around the dangerous goods vehicle under the condition that a person does not know.

In the prior art, when detecting the safety condition around the vehicle carrying the dangerous chemicals, the identification and detection work is often carried out manually so as to ensure that the situation that the danger is caused does not exist around the vehicle carrying the dangerous chemicals.

However, when identification detection is performed manually, if there are a plurality of vehicles carrying dangerous chemicals, detection is missed, and it is not guaranteed that all vehicles carrying dangerous chemicals are completely detected. Therefore, how to cover each vehicle carrying hazardous chemicals when detecting according to the vehicle carrying hazardous chemicals is a problem to be solved urgently.

Disclosure of Invention

In view of this, the safety detection method, device, equipment and storage medium provided by the embodiment of the application can know the position information of the dangerous chemical vehicle in the target area in real time, identify potential abnormal conditions, take corresponding measures in time to handle the abnormal conditions, ensure the safety of the target area, improve the safety monitoring capability of the dangerous chemical vehicle in the target area, help prevent accidents and protect the safety of personnel and property. The security detection method, the security detection device, the security detection equipment and the storage medium provided by the embodiment of the application are realized in the following way:

The safety detection method provided by the embodiment of the application comprises the following steps:

determining the position of a dangerous chemical vehicle, wherein the dangerous chemical vehicle comprises a vehicle for storing dangerous chemicals;

acquiring temperatures of respective objects in a target area including a position of the hazardous chemical vehicle;

when the temperature of any object exceeds a preset safety temperature, acquiring an image corresponding to the target area;

extracting features of the image corresponding to the target area to obtain image features;

and analyzing the image characteristics to obtain a safety detection result, wherein the safety detection result comprises an abnormal condition and a normal condition.

In some embodiments, the analyzing the image features to obtain a security detection result includes:

performing behavior recognition on the image features to obtain a behavior recognition result, wherein the behavior recognition result is used for indicating whether the image corresponding to the target area comprises preset abnormal behaviors or not;

and determining the safety detection result according to the behavior recognition result, wherein the safety detection result is an abnormal condition when the behavior recognition result indicates that the image corresponding to the target area comprises the preset abnormal behavior.

In some embodiments, the performing behavior recognition on the image features to obtain a behavior recognition result includes:

inputting the image features into a preset behavior detection model to obtain the behavior detection result, wherein the preset behavior detection model is obtained by training an initial behavior detection model according to a plurality of preset abnormal behaviors and the image features corresponding to each preset abnormal behavior.

In some embodiments, the inputting the image feature into a preset behavior detection model to obtain the behavior detection result includes:

vectorizing the image features to obtain image feature vectors corresponding to the image features;

acquiring a human body image feature vector used for representing a human body in the image feature vector and a high-temperature object image feature vector used for representing a high-temperature object with temperature higher than a preset threshold in the image feature vector;

and calculating the distance between the human body and the high-temperature object according to the human body image feature vector and the high-temperature object image feature vector, and determining the behavior detection result as abnormal behavior when the distance between the human body and the high-temperature object is within a preset range.

performing scene recognition according to the image features to obtain a scene recognition result, wherein the scene recognition result is used for indicating whether the target area is in a fire scene or not;

and determining the safety detection result according to the scene recognition result, wherein the safety detection result is an abnormal condition when the scene recognition result indicates that the image corresponding to the target area comprises the preset abnormal scene.

In some embodiments, the acquiring the image corresponding to the target area when the temperature of any object exceeds a preset safe temperature includes:

determining coordinates of a target object, wherein the target object is an object with temperature exceeding the preset safety temperature;

determining an object area from the target area, wherein the object area is determined according to the coordinate of the target object and a preset distance range;

and acquiring an image of the object region, wherein the image corresponding to the target region is the image of the object region.

In some embodiments, the analyzing the image features to obtain a security detection result, after the security detection result is an abnormal condition, further includes:

And when the safety detection result is an abnormal condition, alarming is carried out based on the abnormal condition, wherein the alarming mode comprises sound-light alarming or popup window image alarming, and the popup window image comprises an image corresponding to the abnormal condition.

The embodiment of the application provides a safety detection device, including:

the determining module is used for determining the position of a dangerous chemical vehicle, wherein the dangerous chemical vehicle comprises a vehicle for storing dangerous chemicals;

an acquisition module for acquiring a temperature of each object in a target area including a position of the hazardous chemical vehicle;

the acquisition module is also used for acquiring an image corresponding to the target area when the temperature of any object exceeds a preset safety temperature;

the extraction module is used for extracting the characteristics of the image corresponding to the target area to obtain the image characteristics;

the analysis module is used for analyzing the image characteristics to obtain a safety detection result, wherein the safety detection result comprises an abnormal condition and a normal condition.

The computer device provided by the embodiment of the application comprises a memory and a processor, wherein the memory stores a computer program capable of running on the processor, and the processor realizes the method described by the embodiment of the application when executing the program.

The computer readable storage medium provided in the embodiments of the present application stores a computer program thereon, which when executed by a processor implements the method provided in the embodiments of the present application.

According to the safety detection method, the safety detection device, the computer equipment and the computer readable storage medium, through determining the position of the dangerous chemical vehicle, the dangerous chemical vehicle comprises a vehicle for storing dangerous chemicals, the temperature of each object in a target area comprising the position of the dangerous chemical vehicle is obtained, when the temperature of any object exceeds a preset safety temperature, an image corresponding to the target area is obtained, feature extraction is carried out on the image corresponding to the target area, image features are obtained, the image features are analyzed, a safety detection result is obtained, and the safety detection result comprises abnormal conditions and normal conditions. Therefore, the position information of the dangerous chemical vehicle in the target area can be known in real time, potential abnormal conditions are identified, corresponding measures are timely taken to handle the abnormal conditions, the safety of the target area is guaranteed, the safety monitoring capability of the dangerous chemical vehicle in the target area is improved, accident occurrence is prevented, and the safety of personnel and property is protected.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and, together with the description, serve to explain the technical aspects of the application.

Fig. 1 is an application scenario diagram of a security detection method disclosed in an embodiment of the present application;

FIG. 2 is a schematic flow chart of a security detection method disclosed in an embodiment of the present application;

FIG. 3 is a general flow chart of a security detection method disclosed in an embodiment of the present application;

FIG. 4 is a schematic structural diagram of a security detection device according to an embodiment of the present disclosure;

fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present application.

Detailed Description

For the purposes, technical solutions and advantages of the embodiments of the present application to be more apparent, the specific technical solutions of the present application will be described in further detail below with reference to the accompanying drawings in the embodiments of the present application. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the present application.

In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is to be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments and can be combined with one another without conflict.

It should be noted that the term "first/second/third" in reference to the embodiments of the present application is used to distinguish similar or different objects, and does not represent a specific ordering of the objects, it being understood that the "first/second/third" may be interchanged with a specific order or sequence, as permitted, to enable the embodiments of the present application described herein to be implemented in an order other than that illustrated or described herein.

In view of this, the embodiment of the present application provides a security detection method that is applied to an intelligent image capturing apparatus. Fig. 1 is an application scenario diagram of a security detection method according to an embodiment. As shown in fig. 1, a smart image capturing apparatus 10 captures a vehicle 11, which may include, but is not limited to, a camera, a video camera, a scanner, a thermal infrared imager, a laser scanner, an optical sensor, and the like. The intelligent image pickup apparatus 10 acquires picture information within a certain area around the vehicle 11, and then processes the picture information. The functions achieved by the method may be achieved by a processor in the smart camera device 10 invoking program code, which of course may be stored in a computer storage medium, it being seen that the smart camera device 10 comprises at least a processor and a storage medium.

Fig. 2 is a schematic implementation flow chart of a security detection method according to an embodiment of the present application. A security detection method shown in fig. 2 may be applied in an application scenario of the security detection method shown in fig. 1, and as shown in fig. 2, the method may include the following steps 201 to 205:

in step 201, the position of a hazardous chemical vehicle is determined, wherein the hazardous chemical vehicle comprises a vehicle for storing hazardous materials.

In the embodiment of the application, the vehicle 11 may acquire the position information of the dangerous chemical vehicle through a positioning technology such as a GPS, the vehicle 10 sends the positioning information to the intelligent image capturing device 10, and the intelligent image capturing device 10 determines the corresponding target area according to the position of the dangerous chemical vehicle in the subsequent safety detection process according to the positioning information sent by the vehicle 11.

Step 202, acquiring temperatures of respective objects in a target area including a position of a hazardous chemical vehicle.

In the embodiment of the present application, the intelligent image pickup apparatus 10 may acquire the temperatures of the respective objects in the target area using an apparatus such as a thermal infrared imager. In order to ensure accuracy, an appropriate temperature threshold may be set in the intelligent image pickup apparatus 10, and when the temperature of a certain object exceeds the threshold, an abnormal situation is considered to exist.

And 203, acquiring an image corresponding to the target area when the temperature of any object exceeds the preset safety temperature.

In the embodiment of the present application, when the temperature of any one object exceeds the preset safe temperature, the intelligent image capturing apparatus 10 automatically determines whether or not there is a temperature abnormality. If an abnormal condition exists, performing the next processing; otherwise, continuing to monitor.

As an example, when the temperature of any object exceeds a preset safe temperature, acquiring an image corresponding to a target area includes: and determining the coordinates of a target object, wherein the target object is an object with the temperature exceeding the preset safe temperature. Optionally, a temperature sensor or a thermal imager and other devices are installed in the target area to monitor the temperature of each object in real time.

And determining an object with the temperature exceeding the preset safety temperature through data processing and analysis, and obtaining corresponding coordinate information of the object.

Further, an object area is determined from the target area, and the object area is determined according to the coordinates of the target object and a preset distance range. Optionally, according to the coordinate information of the target object, in combination with a preset distance range, determining an area around the target object as the object area. An appropriate distance range may be set according to specific requirements and scenes, for example, a radius or a side length may be set with the target object as the center.

Further, an image of the target area is acquired, and the image corresponding to the target area is the image of the target area. Optionally, a camera or other image acquisition device is configured to ensure coverage of the object area within the target area. And adjusting parameters such as the position, the angle, the focal length and the like of the camera so as to obtain a clear and comprehensive object area image. Multiple cameras can be used for multi-angle and multi-view image acquisition as required, so that the accuracy and the integrity of images are improved. And preprocessing the acquired image, including denoising, enhancing contrast, adjusting brightness and the like, so as to improve the effect of subsequent feature extraction. And storing the processed image in a local storage or cloud storage mode and the like so as to facilitate subsequent feature extraction and analysis.

And 204, extracting features of the image corresponding to the target area to obtain image features.

In the embodiment of the present application, when there is a temperature abnormality, the intelligent image capturing apparatus 10 automatically acquires an image corresponding to the target area. Here, an image may be acquired using a camera or the like. In order to ensure the quality of an image, an appropriate image resolution and frame rate may be set in the smart camera device 10.

The intelligent image pickup apparatus 10 can perform feature extraction on the acquired image using computer vision technology. Further, the characteristics of the color, texture, shape, etc. of the image can be extracted. In order to improve the accuracy of feature extraction, image recognition and classification may be performed using techniques such as deep learning.

And 205, analyzing the image characteristics to obtain a safety detection result, wherein the safety detection result comprises an abnormal condition and a normal condition.

In the embodiment of the present application, the smart camera apparatus 10 analyzes the extracted image features to obtain a security detection result. Image features may be classified and predicted using techniques such as machine learning. If an abnormal condition exists in the image, such as a high-temperature object appears at an improper position, the abnormal condition is considered to exist; otherwise, the normal condition is considered.

And outputting the obtained safety detection result so that relevant personnel can take corresponding measures in time. The detection result can be sent to related personnel by using a mobile phone APP, an email and the like. Meanwhile, an alarm mechanism may be provided in the intelligent image pickup apparatus 10, and when an abnormal situation exists, an alarm is automatically triggered and a relevant person is notified.

As an example, analyzing the image features to obtain a security detection result includes: and carrying out behavior recognition on the image characteristics to obtain a behavior recognition result, wherein the behavior recognition result is used for indicating whether the image corresponding to the target area comprises preset abnormal behaviors or not. Optionally, a sequence of images containing normal and abnormal behavior is acquired from the security surveillance video. These images may be labeled for training of the model by manual labeling or automatic labeling. Deep learning models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), suitable for behavior recognition tasks are selected. The use of pre-trained models and fine tuning based thereon is also contemplated. The acquired image dataset is divided into a training set and a verification set, the training set is used for training the model, and the performance of the model is evaluated through the verification set. Common optimization algorithms, such as random gradient descent (SGD), may be employed to update the model parameters to minimize the loss function. Optionally, a temperature sensor is installed on the hazardous chemical vehicle to monitor the temperature of each object in the vehicle in real time. When the temperature of any one object exceeds the preset safe temperature, triggering the operation of image acquisition. And a video monitoring system is arranged in the dangerous chemical vehicle to monitor the condition in the vehicle in real time. When the temperature exceeds the safety range, the video recording can be automatically triggered, and the image corresponding to the target area is acquired.

The acquired image is preprocessed, such as resizing, cropping, denoising, etc., to enhance the effect of subsequent feature extraction.

The features of the image may be extracted using conventional computer vision methods such as color histograms, texture features, shape descriptors, etc. Meanwhile, a convolution layer or a full connection layer in the deep learning model can be used as a feature extractor, and the image can be input into the model to obtain high-level feature representation.

As an example, performing behavior recognition on the image features to obtain a behavior recognition result includes: inputting the image features into a preset behavior detection model to obtain a behavior detection result, wherein the preset behavior detection model is obtained by training an initial behavior detection model according to a plurality of preset abnormal behaviors and the image features corresponding to each preset abnormal behavior. Optionally, a sequence of images containing normal and abnormal behavior is acquired from a surveillance video around the hazardous chemical vehicle. Care is taken to preserve privacy and data security during acquisition. The acquired data needs to be preprocessed, such as image enhancement, cropping, and scaling, for training of the model. Deep learning models, such as Convolutional Neural Networks (CNNs) or Recurrent Neural Networks (RNNs), suitable for behavior recognition tasks are selected. The acquired image dataset is divided into a training set and a verification set, the training set is used for training the model, and the performance of the model is evaluated through the verification set. Common optimization algorithms, such as random gradient descent (SGD), may be employed to update the model parameters to minimize the loss function.

The preset behavior detection model is obtained by training the initial behavior detection model according to a plurality of preset abnormal behaviors and image features corresponding to each preset abnormal behavior. The idea of transfer learning can be employed, using an already trained behavior recognition model as a base model, and then fine tuning is performed on the basis of the base model in order to better adapt to specific abnormal behavior detection tasks. In the training process, the preset abnormal behavior needs to be marked and used for training a model together with the normal behavior.

Inputting the image features into a preset behavior detection model to obtain a behavior detection result. The behavior can be classified by a softmax classifier, and other classifiers such as a Support Vector Machine (SVM) can be also used. In the course of behavior recognition, attention is paid to the quality and sharpness of the image, as well as the size and position of the target area.

And determining a safety detection result according to the behavior recognition result. When the behavior identification result indicates that the image corresponding to the target area comprises the preset abnormal behavior, the safety detection result is an abnormal condition. In the process of determining the safety detection result, the accuracy and the false alarm rate of behavior identification, and the timely response and the processing capacity to abnormal behaviors need to be considered.

As an example, inputting the image feature into a preset behavior detection model to obtain a behavior detection result, including: and carrying out vectorization processing on the image features to obtain image feature vectors corresponding to the image features. Alternatively, the image corresponding to the target region is converted into a fixed length feature vector using image processing techniques such as Convolutional Neural Network (CNN) or other feature extraction algorithms. These feature vectors may contain information on color, texture, shape, etc.

Further, a human body image feature vector used for representing a human body in the image feature vector and a high-temperature object image feature vector used for representing a high-temperature object with the temperature higher than a preset threshold value in the image feature vector are obtained. Optionally, sub-feature vectors for characterizing the human body and the high temperature object are extracted from the image feature vectors. The feature vectors of the human body may be located and extracted using a target detection algorithm or a human body posture estimation algorithm. Meanwhile, a temperature sensor or an infrared thermal imaging technology can be used for acquiring the position information of the high-temperature object, and corresponding feature vectors can be extracted.

Further, according to the human body image feature vector and the high-temperature object image feature vector, calculating the distance between the human body and the high-temperature object, and determining the behavior detection result as abnormal behavior when the distance between the human body and the high-temperature object is within a preset range. Optionally, according to the distance between the human body image feature vector and the high-temperature object image feature vector, the calculation can be performed by adopting measurement methods such as euclidean distance, cosine similarity and the like. The specific calculation method can be selected according to actual requirements and data characteristics.

And judging in a preset range according to the distance between the human body and the high-temperature object. Further, a threshold value may be set, and when the distance is smaller than the threshold value, abnormal behavior is determined; when the distance is equal to or greater than the threshold value, normal behavior is determined. According to the judgment result, the behavior detection result is determined to be abnormal behavior or normal behavior.

Further, a safety detection result is determined according to the behavior recognition result, wherein the safety detection result is an abnormal condition when the behavior recognition result indicates that the image corresponding to the target area comprises a preset abnormal behavior. Optionally, according to the output result of the behavior recognition model, an appropriate threshold may be set to determine whether a preset abnormal behavior exists. When the confidence of the behavior recognition result exceeds a set threshold, the abnormal behavior is considered to exist. The image features can be fused with other sensor data (e.g., temperature, sound, etc.) to improve the accuracy of the security detection results. Fusion methods such as weighted fusion, feature level fusion, or decision level fusion may be used. And displaying the safety detection result to related personnel in a visual mode, such as displaying the position and time information of abnormal behaviors on a monitoring interface.

When abnormal conditions exist, an alarm mechanism is triggered, and related personnel are reminded in a sound, light or text prompting mode and the like. Different levels of alarms may be set to take corresponding countermeasures depending on severity.

As an example, analyzing the image features to obtain a security detection result includes: and carrying out scene recognition according to the image features to obtain a scene recognition result, wherein the scene recognition result is used for indicating whether the target area is in a fire scene or not. Optionally, target area image data including hazardous chemical vehicles is collected and subjected to pre-processing operations such as denoising, resizing and formatting, etc., for subsequent processing use.

And training a scene recognition model by using the marked image data set through a deep learning method. Common deep learning models include Convolutional Neural Networks (CNNs) and pre-training models (e.g., res net, VGG, etc.), which enable the models to accurately identify different scene categories by training a large amount of image data.

And inputting the preprocessed target area image into a trained scene recognition model, and obtaining a scene recognition result of the image through forward propagation calculation. The result may be a probability distribution representing the probability that the image belongs to different scene categories.

And judging whether the target area is in a preset abnormal scene, such as a fire scene, according to the scene identification result. A threshold may be set that when the probability of a fire scene exceeds the threshold, an abnormal scene is considered to be present in the image.

Further, a safety detection result is determined according to the scene recognition result, wherein when the scene recognition result indicates that the image corresponding to the target area comprises a preset abnormal scene, the safety detection result is an abnormal situation. Optionally, a security detection result is determined according to the judgment result. If a preset abnormal scene exists in the image, namely a fire scene, the safety detection result is determined to be an abnormal situation; otherwise, the security detection result is determined as normal.

As an example, the image feature is analyzed to obtain a security detection result, and after the security detection result is an abnormal condition, the method further includes: when the safety detection result is an abnormal condition, alarming is carried out based on the abnormal condition, wherein the alarming mode comprises sound-light alarming or popup window image alarming, and the popup window image comprises an image corresponding to the abnormal condition. Alternatively, the system may be connected to a speaker or alarm to emit a high-loudness, harsh audible alert that attracts attention.

Alternatively, the system can draw attention by connecting a flashing light or a warning light to flash or continuously light.

Optionally, the system may pop up a window on the monitor terminal, display an image corresponding to the abnormal situation, and alert the monitor personnel or related staff in a striking manner.

In the popup window image alarm, the system can label and explain the images of the abnormal situation so as to help personnel to better understand and judge the nature and severity of the abnormal situation.

The positions and the number of the sound alarms, the flashing lights and other devices are reasonably arranged according to the size and the layout of the target area so as to ensure that the alarm can be perceived in the whole area.

The loudness of the audible alarm should be high enough to attract people's attention, and the frequency of the blinking light should be moderate, so that the audible alarm can attract attention and is not too glaring.

The popup window image can be clearly visible on the monitoring terminal and displayed over other tasks or windows to ensure timely notice of alarm information.

According to the method, the position of the dangerous chemical vehicle is determined, the dangerous chemical vehicle comprises a vehicle for storing dangerous chemicals, the temperature of each object in a target area comprising the position of the dangerous chemical vehicle is obtained, when the temperature of any object exceeds the preset safety temperature, an image corresponding to the target area is obtained, the image corresponding to the target area is subjected to feature extraction, the image features are obtained, the image features are analyzed, the safety detection result is obtained, and the safety detection result comprises abnormal conditions and normal conditions. Through this application can realize real-time supervision and analysis to danger article vehicle surrounding environment, in time discovers abnormal conditions, such as conflagration, high temperature etc to reduce the probability of accident emergence, improve the security. Meanwhile, the method and the device can also be used for identifying behaviors, such as smoking around dangerous chemical vehicles, so that safety is further improved. According to the method and the device, through an automatic image processing and behavior recognition technology, real-time monitoring and analysis of the surrounding environment of the dangerous chemical vehicle can be achieved, the tedious work of manual patrol is avoided, and the working efficiency is improved. The automation feature allows for more intelligent and efficient systems. The method and the device can detect temperature abnormality, conduct behavior recognition and scene recognition, and further improve detection accuracy and reliability. The method can be applied to safety detection of the surrounding environment of various dangerous chemical vehicles, and has wide practicability and application prospect. When the safety detection result is abnormal, different alarm modes, such as audible and visual alarm or popup window image alarm, can be selected according to actual needs. The flexible and various alarm modes can enable people to more intuitively know abnormal conditions and timely take corresponding measures to avoid accidents.

An exemplary application of the embodiments of the present application in a practical application scenario will be described below.

Fig. 3 is a general flow chart of a security detection method according to an embodiment of the present application. A security detection method shown in fig. 3 may be applied in an application scenario of the security detection method shown in fig. 1, and as shown in fig. 3, the method includes the following steps 301 to 306:

step 301, determining the position of the dangerous chemical vehicle, wherein the position of the dangerous chemical vehicle is the position of the dangerous chemical vehicle in a parking area when the dangerous chemical vehicle is parked.

In the embodiment of the application, the vehicle 11 can accurately acquire the position of the vehicle 11 according to the positioning device carried by the vehicle 11, wherein in the positioning of the vehicle 11, only the positioning of the dangerous chemical vehicle is required to be acquired, so that basic data is provided for subsequent monitoring and processing.

And 302, acquiring the temperature of an object in a certain area range around the dangerous chemical vehicle, and determining subsequent emergency judgment according to the temperature.

In the embodiment of the application, the intelligent imaging device 10 acquires the temperature of the object in a certain area range around the dangerous chemical vehicle, and is helpful for judging whether the condition exceeding the preset safety temperature threshold exists or not, so that measures are taken in time.

In step 303, when the temperature of the object in a certain area around the dangerous chemical vehicle exceeds a preset safety temperature threshold, image information of the object is acquired.

In the embodiment of the application, the intelligent camera device 10 acquires the image information of the object with the temperature exceeding the preset safety temperature threshold value, and provides necessary data support for the follow-up actions.

And step 304, extracting features of the image information of the object to obtain corresponding image features.

And 305, performing feature analysis on the image features to obtain a safety detection result, wherein the safety detection result is used for indicating the safety condition of a certain area range around the dangerous chemical vehicle, and when the safety detection result is an abnormal condition, performing subsequent processing.

In the embodiment of the application, the intelligent camera device 10 performs feature extraction and analysis on the image information to obtain a safety detection result, and timely finds out abnormal conditions to provide decision basis for subsequent processing.

In step 306, in the case that the safety detection result is an abnormal condition, an alarm processing is performed, where the alarm processing may include an audible and visual alarm or a popup window image alarm.

In the embodiment of the application, the intelligent camera device 10 performs alarm processing under the condition that the safety detection result is abnormal, and timely reminds relevant personnel to take measures, so that the safety and reliability of dangerous chemical transportation are ensured.

According to the embodiment of the application, the temperature and the image information are monitored in real time in a certain area range around the dangerous chemical vehicle, and the safety detection result is obtained through feature extraction and analysis. When the safety detection result is abnormal, alarm processing is performed in time, so that the safety and reliability of dangerous chemical transportation are improved.

It should be understood that, although the steps in the flowcharts described above are shown in order as indicated by the arrows, these steps are not necessarily performed in order as indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described above may include a plurality of sub-steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of execution of the sub-steps or stages is not necessarily sequential, but may be performed alternately or alternately with at least a part of the sub-steps or stages of other steps or other steps.

Based on the foregoing embodiments, the embodiments of the present application provide a security detection device, where the security detection device includes each module included, and each unit included in each module may be implemented by a processor; of course, the method can also be realized by a specific logic circuit; in an implementation, the processor may be a Central Processing Unit (CPU), a Microprocessor (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.

Fig. 4 is a schematic structural diagram of a security detection device provided in an embodiment of the present application, as shown in fig. 4, the device 400 includes a determining module 401, an obtaining module 402, an extracting module 403, and an analyzing module 404, where:

a determining module 401, configured to determine a position of a hazardous chemical vehicle, where the hazardous chemical vehicle includes a vehicle storing hazardous articles;

an acquisition module 402 for acquiring a temperature of each object in a target area including a position of a hazardous chemical vehicle;

the acquiring module 402 is further configured to acquire an image corresponding to the target area when the temperature of any object exceeds a preset safe temperature;

the extracting module 403 is configured to perform feature extraction on an image corresponding to the target area to obtain an image feature;

the analysis module 404 is configured to analyze the image features to obtain a security detection result, where the security detection result includes an abnormal condition and a normal condition.

In some embodiments, the determining module 401 is further configured to perform behavior recognition on the image feature to obtain a behavior recognition result, where the behavior recognition result is used to indicate whether the image corresponding to the target area includes a preset abnormal behavior;

the determining module 401 is further configured to determine a security detection result according to the behavior recognition result, where the security detection result is an abnormal situation when the behavior recognition result indicates that the image corresponding to the target area includes a preset abnormal behavior.

In some embodiments, the analysis module 404 is further configured to input the image features into a preset behavior detection model to obtain a behavior detection result, where the preset behavior detection model is obtained by training the initial behavior detection model according to a plurality of preset abnormal behaviors and image features corresponding to each preset abnormal behavior.

In some embodiments, the obtaining module 402 is further configured to perform vectorization processing on the image features to obtain image feature vectors corresponding to the image features;

the obtaining module 402 is further configured to obtain a human body image feature vector used for representing a human body from the image feature vectors and a high temperature object image feature vector used for representing a high temperature object with a temperature higher than a preset threshold from the image feature vectors;

the analysis module 404 is further configured to calculate a distance between the human body and the high-temperature object according to the human body image feature vector and the high-temperature object image feature vector, and determine that the behavior detection result is abnormal behavior when the distance between the human body and the high-temperature object is within a preset range.

In some embodiments, the analysis module 404 is further configured to perform scene recognition according to the image features, to obtain a scene recognition result, where the scene recognition result is used to indicate whether the target area is in a fire scene;

The analysis module 404 is further configured to determine a security detection result according to the scene recognition result, where the security detection result is an abnormal situation when the scene recognition result indicates that the image corresponding to the target area includes a preset abnormal scene.

In some embodiments, the determining module 401 is further configured to determine coordinates where a target object is located, where the target object is an object whose temperature exceeds a preset safe temperature;

the determining module 401 is further configured to determine an object area from the target area, where the object area is an area determined according to coordinates where the target object is located and a preset distance range;

the acquiring module 402 is further configured to acquire an image of the object area, where the image corresponding to the target area is the image of the object area.

In some embodiments, the analysis module 404 is further configured to, when the security detection result is an abnormal situation, alarm based on the abnormal situation, where the alarm mode includes an audible and visual alarm or a popup image alarm, and the popup image includes an image corresponding to the abnormal situation.

The description of the apparatus embodiments above is similar to that of the method embodiments above, with similar advantageous effects as the method embodiments. For technical details not disclosed in the device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

It should be noted that, in the embodiment of the present application, the division of modules by a security detection device shown in fig. 4 is schematic, and is merely a logic function division, and there may be another division manner in practical implementation. In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units. Or in a combination of software and hardware.

It should be noted that, in the embodiment of the present application, if the method is implemented in the form of a software functional module, and sold or used as a separate product, the method may also be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, an optical disk, or other various media capable of storing program codes. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.

The embodiment of the application provides a computer device, which may be a server, and an internal structure diagram thereof may be shown in fig. 5. The computer device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the computer device is configured 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, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing data. The network interface of the computer device is used for communicating with an external terminal through a network connection. Which computer program, when being executed by a processor, carries out the above-mentioned method.

The present embodiment provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of the method provided in the above embodiment.

The present application provides a computer program product comprising instructions which, when run on a computer, cause the computer to perform the steps of the method provided by the method embodiments described above.

It will be appreciated by those skilled in the art that the structure shown in fig. 5 is merely a block diagram of some of the structures associated with the present application and is not limiting of the computer device to which the present application may be applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In one embodiment, a security detection device provided herein may be implemented in the form of a computer program that is executable on a computer device as shown in fig. 5. The memory of the computer device may store the various program modules that make up the apparatus. The computer program of each program module causes a processor to perform the steps in the methods of each embodiment of the present application described in the present specification.

It should be noted here that: the description of the storage medium and apparatus embodiments above is similar to that of the method embodiments described above, with similar benefits as the method embodiments. For technical details not disclosed in the storage medium, storage medium and device embodiments of the present application, please refer to the description of the method embodiments of the present application for understanding.

It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" or "some embodiments" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" or "in some embodiments" in various places throughout this specification are not necessarily referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application. The foregoing embodiment numbers of the present application are merely for describing, and do not represent advantages or disadvantages of the embodiments. The foregoing description of various embodiments is intended to highlight differences between the various embodiments, which may be the same or similar to each other by reference, and is not repeated herein for the sake of brevity.

The term "and/or" is herein merely an association relation describing associated objects, meaning that there may be three relations, e.g. object a and/or object B, may represent: there are three cases where object a alone exists, object a and object B together, and object B alone exists.

It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments are merely illustrative, and the division of the modules is merely a logical function division, and other divisions may be implemented in practice, such as: multiple modules or components may be combined, or may be integrated into another system, or some features may be omitted, or not performed. In addition, the various components shown or discussed may be coupled or directly coupled or communicatively coupled to each other via some interface, whether indirectly coupled or communicatively coupled to devices or modules, whether electrically, mechanically, or otherwise.

The modules described above as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules; can be located in one place or distributed to a plurality of network units; some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

In addition, each functional module in each embodiment of the present application may be integrated in one processing unit, or each module may be separately used as one unit, or two or more modules may be integrated in one unit; the integrated modules may be implemented in hardware or in hardware plus software functional units.

Those of ordinary skill in the art will appreciate that: all or part of the steps for implementing the above method embodiments may be implemented by hardware related to program instructions, and the foregoing program may be stored in a computer readable storage medium, where the program, when executed, performs steps including the above method embodiments; and the aforementioned storage medium includes: a mobile storage device, a Read Only Memory (ROM), a magnetic disk or an optical disk, or the like, which can store program codes.

Alternatively, the integrated units described above may be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or part contributing to the related art, and the computer software product may be stored in a storage medium, including several instructions for causing an electronic device to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a removable storage device, a ROM, a magnetic disk, or an optical disk.

The methods disclosed in the several method embodiments provided in the present application may be arbitrarily combined without collision to obtain a new method embodiment.

The features disclosed in the several product embodiments provided in the present application may be combined arbitrarily without conflict to obtain new product embodiments.

The features disclosed in the several method or apparatus embodiments provided in the present application may be arbitrarily combined without conflict to obtain new method embodiments or apparatus embodiments. The foregoing is merely an embodiment of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered in the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.

Claims

1. A security detection method, the method comprising:

2. The method of claim 1, wherein analyzing the image features to obtain a security detection result comprises:

3. The method according to claim 2, wherein the performing behavior recognition on the image feature to obtain a behavior recognition result includes:

4. A method according to claim 3, wherein said inputting the image features into a preset behavior detection model to obtain the behavior detection result comprises:

5. The method according to claim 1 or 2, wherein the analyzing the image features to obtain a security detection result comprises:

6. The method according to claim 1, wherein the acquiring the image corresponding to the target area when the temperature of any object exceeds a preset safety temperature includes:

7. The method according to claim 1, wherein the analyzing the image features to obtain a security detection result, after the security detection result is an abnormal condition, further comprises:

8. A security detection device, comprising:

9. A computer device comprising a memory and a processor, the memory storing a computer program executable on the processor, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the program is executed.

10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method according to any one of claims 1 to 7.