CN116188415A - Method and device for detecting entity in carrier and electronic equipment - Google Patents
Method and device for detecting entity in carrier and electronic equipment Download PDFInfo
- Publication number
- CN116188415A CN116188415A CN202310126366.2A CN202310126366A CN116188415A CN 116188415 A CN116188415 A CN 116188415A CN 202310126366 A CN202310126366 A CN 202310126366A CN 116188415 A CN116188415 A CN 116188415A
- Authority
- CN
- China
- Prior art keywords
- carrier
- target
- level signal
- entity
- image data
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10048—Infrared image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20081—Training; Learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20084—Artificial neural networks [ANN]
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The application provides a method and a device for detecting an entity in a carrier and electronic equipment, wherein the method for detecting the entity in the carrier comprises the following steps: determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor; if so, acquiring image data of the target bearing object; carrying out data frame reorganization on the infrared induction level signals and the image data according to a preset data frame format, and determining a first target data frame of a target carrier; and carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier recognition model, and determining whether an entity to be detected exists in the target carrier. The method and the device realize automatic detection of the entity to be detected in the target carrier, reduce labor cost and improve detection efficiency of the state of the entity to be detected.
Description
Technical Field
The present disclosure relates to the field of image recognition technologies, and in particular, to a method and an apparatus for detecting an entity in a carrier, and an electronic device.
Background
Along with the continuous development of information technology, more and more materials are used, and meanwhile, the rest materials are required to be orderly stored, in the process of storing and managing the materials, the traditional material entity storage requires a worker to detect and patrol the bearing objects of the material entity, and often requires the worker to carry out real-time tracking record on the storage of some important material entities, if the tracking is not in place, the storage state and storage position of the material entity are not clear, and further the patrol efficiency of the stored material entity is low.
Disclosure of Invention
In view of this, the present application aims to provide a method, an apparatus and an electronic device for detecting an entity in a carrier, which realize automatic detection of an entity to be detected in a target carrier, and improve the detection efficiency of the state of the entity to be detected while reducing labor cost.
The embodiment of the application provides a method for detecting an entity in a carrier, which comprises the following steps:
determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor;
if so, acquiring the image data of the target bearing object;
carrying out data frame reorganization on the infrared induction level signals and the image data according to a preset data frame format, and determining a first target data frame of the target carrier;
and carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier recognition model, and determining whether an entity to be detected exists in the target carrier.
Further, the type of the infrared sensing level signal includes a high level signal and a low level signal, and determining whether a target carrier exists in the to-be-detected area according to the type of the infrared sensing level signal acquired by the infrared sensor includes:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected;
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
Further, the step of reorganizing the data frames of the infrared sensing level signal and the image data according to a preset data frame format to determine a first target data frame of the target carrier includes:
and carrying out data frame recombination on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
Further, after determining whether the target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor, the method for detecting the entity in the carrier further includes:
if the target load object does not exist, carrying out data frame recombination on the infrared induction level signal according to a preset format frame type, and determining a second target data frame of the target load object;
and carrying out data analysis on the second target data frame, and storing and displaying the analyzed infrared induction level signal.
Further, the trained carrier recognition model is determined by:
acquiring image data of a sample carrier and a label of the image data of the sample carrier, wherein the label is used for representing real type information of a sample entity in the sample carrier;
performing data enhancement processing on the image data of the sample carrier to determine enhanced image data of the carrier;
inputting the enhanced image data into an initial carrier recognition model for training, and determining preset type information of sample entities in the sample carrier;
and when the loss value between the preset type information of the sample entity and the real type information of the sample entity is smaller than a preset threshold value, training is stopped, and a trained carrier recognition model is determined.
Further, the performing data enhancement processing on the image data of the sample carrier, determining enhanced image data of the carrier includes:
and carrying out translation, scaling, rotation, cutting, overturning and splicing processing on the image data of the sample carrier, and determining the enhanced image data of the carrier.
The embodiment of the application also provides a device for detecting the entity in the carrier, which comprises:
the first determining module is used for determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor;
the acquisition module is used for acquiring the image data of the target bearing object if the target bearing object exists;
the second determining module is used for carrying out data frame reorganization on the infrared induction level signal and the image data according to a preset data frame format to determine a first target data frame of the target carrier;
the identification module is used for carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier identification model and determining whether an entity to be detected exists in the target carrier.
Further, the type of the infrared sensing level signal includes a high level signal and a low level signal, and the first determining module is specifically configured to:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected;
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
The embodiment of the application also provides electronic equipment, which comprises: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory communicate through the bus when the electronic device is running, and the machine-readable instructions are executed by the processor to perform the steps of the method for detecting the entity in the carrier.
Embodiments of the present application also provide a computer readable storage medium having stored thereon a computer program which, when executed by a processor, performs the steps of a method for detecting an entity in a carrier as described above.
Compared with the prior art, the method, the device and the electronic equipment for detecting the entity in the carrier provided by the embodiment of the application are used for judging whether the entity to be detected exists in the target carrier or not by combining the infrared induction level signals acquired by the infrared sensor and the image data and generating the first target data frame, so that the automatic analysis and management of the storage state of the entity to be detected in the target carrier are realized, the labor cost is reduced, and the management efficiency of the entity to be detected is improved.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 shows one of flowcharts of a method for detecting an entity in a carrier according to an embodiment of the present application;
FIG. 2 is a second flowchart of a method for detecting an entity in a carrier according to an embodiment of the present disclosure;
fig. 3 is a flowchart illustrating an embodiment of a method for detecting an entity in a carrier according to an embodiment of the present application;
fig. 4 shows one of schematic structural diagrams of a device for detecting an entity in a carrier according to an embodiment of the present application;
FIG. 5 is a schematic diagram showing a second embodiment of a device for detecting an entity in a carrier;
fig. 6 shows a schematic structural diagram of an electronic device according to an embodiment of the present application.
In the figure:
400-detection means of entities in the carrier; 410-a first determination module; 420-an acquisition module; 430-a second determination module; 440-an identification module; 450-a third determination module; 460-a display module; 600-an electronic device; 610-a processor; 620-memory; 630-bus.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. Based on the embodiments of the present application, every other embodiment that a person skilled in the art would obtain without making any inventive effort is within the scope of protection of the present application.
First, application scenarios applicable to the present application will be described. The method and the device can be applied to the technical field of image recognition.
According to research, along with the continuous development of information technology, more and more materials are used, meanwhile, the rest materials are required to be orderly stored, in the process of storing and managing the materials, the traditional material entity storage requires a worker to detect and inspect the load bearing objects of the material entity, and often requires the worker to carry out real-time tracking record on the storage of some important material entities, if the tracking is not in place, the storage state and storage position of the material entity are not clear, and further the inspection efficiency of the stored material entity is low.
Based on the above, the embodiment of the application provides a method, a device and electronic equipment for detecting an entity in a carrier, which realize automatic detection of the entity to be detected in a target carrier, reduce labor cost and improve detection efficiency of the state of the entity to be detected.
Referring to fig. 1, fig. 1 is a flowchart of a method for detecting an entity in a carrier according to an embodiment of the present application. As shown in fig. 1, the method for detecting an entity in a carrier provided in the embodiment of the present application includes the following steps:
s101, determining whether a target carrier exists in a region to be detected according to the type of an infrared induction level signal acquired by an infrared sensor.
In the step, an infrared induction level signal or a switching value signal acquired by an infrared sensor is acquired, and then whether a target carrier exists in a region to be detected is determined according to the type of the infrared induction level signal or the switching value signal acquired by the infrared sensor.
In the foregoing, the application scenario of the embodiment provided in the present application is a space launching site, the target carrier includes, but is not limited to, a warehouse that is the space launching site, and the entity to be detected includes, but is not limited to, space launching materials.
Here, the infrared sensing level signal or the switching value signal is set to two states of 0 and 1, 0 representing a low level signal and 1 representing a high level signal.
The infrared sensor is mainly characterized in that components are used for collecting and integrating multiple paths of signals on a circuit board, and according to the principle of emitting and receiving infrared geminate transistors, high-low level signals are output to indicate whether a target bearing object shields infrared light or not.
Further, the type of the infrared sensing level signal includes a high level signal and a low level signal, and determining whether a target carrier exists in the to-be-detected area according to the type of the infrared sensing level signal acquired by the infrared sensor includes:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected.
If the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
Here, 0 represents a low level signal, which is used to characterize the occlusion of the target carrier between the infrared pair of tubes; 1 represents a high level signal, which is used for representing that no target carrier is blocked between the infrared pair tubes.
S102, if the target load exists, acquiring image data of the target load.
In the step, if the infrared sensing level signal acquired by the infrared sensor is a low level signal and it is determined that the target carrier exists in the area to be detected, the image data of the target carrier needs to be acquired by the image capturing device.
Here, the image capturing apparatus in the embodiment provided in the present application includes, but is not limited to, using a raspberry-set CSI interface standard version camera, supporting 1080p30,720p60, and 640×480p60/90 image capturing.
S103, carrying out data frame recombination on the infrared induction level signal and the image data according to a preset data frame format, and determining a first target data frame of the target carrier.
In the step, under the condition that the existence of a target bearing object in the area to be detected is determined, carrying out data frame recombination on the infrared induction level signal and the image data according to a preset data frame format to determine a first target data frame of the target bearing object.
Optionally, the step of reorganizing the data frames of the infrared sensing level signal and the image data according to a preset data frame format to determine a first target data frame of the target carrier includes:
and carrying out data frame recombination on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
Here, since the data formats of different types of heterogeneous data collected from external hardware devices are different, a general data frame format (a preset data frame format as provided in the embodiment of the present application) is required to unify the different types of heterogeneous data into a data frame of the same format, that is, a first target data frame as provided in the embodiment of the present application.
S104, carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier recognition model, and determining whether an entity to be detected exists in the target carrier.
In this step, the trained carrier recognition model is used to recognize whether there is an entity to be detected in the target carrier.
In the above, after determining whether the entity to be detected exists in the target carrier, the detection result is displayed and stored in a line graph, a bar graph and a visual form, so that the use condition of the entity to be detected and whether the use of the entity to be detected at a certain position is abnormal or not can be conveniently mastered in real time. And alarms in the event of anomalies.
And the user or staff can also query the use history record of the entity to be detected, the number of the current entity to be detected and the like in the stored database in real time.
Here, the type of the trained carrier recognition model may be selected adaptively according to different application scenarios and different requirements, for example, the trained carrier recognition model in the embodiment provided by the application may but is not limited to a YOLOv5l network model, and the trained carrier recognition model in the embodiment provided by the application may improve the recognition accuracy of the entity to be detected, reduce the possibility of false detection, and provide reliable and efficient information of the entity to be detected (space material) for the manager.
The YOLOv5 is a single-stage target detection algorithm, and the algorithm adds some new improvement ideas on the basis of YOLOv4, so that the speed and the precision of the algorithm are improved greatly. The main improvement thought is as follows:
an input end: in the model training stage, a plurality of improvement ideas are provided, and the improvement ideas mainly comprise Mosaic data enhancement, self-adaptive anchor frame calculation and self-adaptive picture scaling.
Reference network: some new ideas in other detection algorithms are fused, and the method mainly comprises the following steps: focus structure and CSP structure.
The negk network: the target detection network often inserts layers between the backband and the last Head output layer, and a fpn+pan structure is added in Yolov 5.
Head output layer: the anchor frame mechanism of the output layer is the same as YOLOv4, the main improvement is the Loss function giou_loss at training and diou_nms of prediction frame screening.
Optionally, the trained carrier recognition model is determined by:
and acquiring image data of the sample carrier and a label of the image data of the sample carrier, wherein the label is used for representing real type information of a sample entity in the sample carrier.
And carrying out data enhancement processing on the image data of the sample carrier, and determining enhanced image data of the carrier.
And inputting the enhanced image data into an initial carrier recognition model for training, and determining preset type information of sample entities in the sample carrier.
And when the loss value between the preset type information of the sample entity and the real type information of the sample entity is smaller than a preset threshold value, training is stopped, and a trained carrier recognition model is determined.
Here, in the training process, parameters of the carrier recognition model may be set as follows: batch-size=3; maximum number of iterations epochs=300; setting the image weight image-weights as true; because there is only one category, single-cls is set to true; the optimizer uses SGD; the initial learning rate lr0 is set to 0.01; the cosine function dynamic reduction learning rate lrf is set to 0.12.
Optionally, the performing data enhancement processing on the image data of the sample carrier, determining enhanced image data of the carrier includes:
and carrying out translation, scaling, rotation, cutting, overturning and splicing processing on the image data of the sample carrier, and determining the enhanced image data of the carrier.
Here, because of different placement positions and illumination, the image data of the sample carrier captured by the camera may be incomplete or color difference due to illumination, so that the image data of the sample carrier needs to be expanded and enhanced, so as to improve the robustness and generalization capability of the trained carrier recognition model.
Wherein the image data of the present carrier is augmented using an image data enhancement library (enhancement) and a Mosic data enhancement tool.
Therefore, the mosaics are enhanced by splicing the four pictures, each picture is provided with the corresponding positioning frame, then a new picture is obtained after the four pictures are spliced, the corresponding positioning frame of the picture is obtained, and then the new picture is transmitted into the neural network for learning, which is equivalent to the transmission of four pictures at a time for learning, so that the background of a detected object is greatly enriched, the calculation of the standardized BN is realized, and the calculation efficiency is improved.
In the above, the image data of the sample carrier is subjected to translation, scaling, rotation, clipping, overturning and splicing processing, and the enhanced image data of the carrier is determined.
Performing a process of randomly changing brightness and a blurring process on the image data of the sample carrier; and the method of Mosic is utilized, four picture fixing areas are intercepted in a matrix mode, and the four picture fixing areas are spliced to form new sample carrier image data, so that a carrier recognition model is facilitated to learn a small target.
Optionally, in the embodiment provided by the present application, after determining whether the target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor, the method for detecting the entity in the carrier further includes:
if the target load object does not exist, carrying out data frame recombination on the infrared induction level signal according to a preset format frame type, and determining a second target data frame of the target load object.
Here, the second target data frame is a data frame containing only the infrared sensing level signal.
And carrying out data analysis on the second target data frame, and storing and displaying the analyzed infrared induction level signal.
Here, if the second target data frame is analyzed to be represented as "0", the infrared induction level signal after the analysis is directly stored and displayed.
Compared with the prior art, the method for detecting the entity in the carrier provided by the embodiment of the application judges whether the entity to be detected exists in the target carrier or not by combining the infrared induction level signals acquired by the infrared sensor and the image data and generating the first target data frame, realizes automatic detection of the entity to be detected in the target carrier, reduces labor cost and improves detection efficiency of the state of the entity to be detected.
In addition, the embodiment provided by the embodiment of the application effectively improves the preparation work before the launching of the space mission by monitoring the position and the state of the entity to be detected in real time.
Referring to fig. 2, fig. 2 is a second flowchart of a method for detecting an entity in a carrier according to an embodiment of the present application. As shown in fig. 2, the method for detecting an entity in a carrier provided in the embodiment of the present application includes the following steps:
s201, determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor.
S202, if the target bearing object exists, acquiring image data of the target bearing object.
And S203, carrying out data frame reorganization on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
In this step, the preset data frame format is used to unify heterogeneous data into a data frame with the same format, and the preset data frame format is shown in table 1:
table 1 preset data frame format
Wherein, preset identification is used for representing the type of infrared induction level signal, and 0 represents low level signal, and 1 represents high level signal.
The acquisition time is used to characterize the current time at which the first target data frame was generated.
The infrared sensing level signal length is used to characterize the number of bytes of the level signal.
The image data length is used to characterize the number of bytes of picture data.
The effective data length is used for representing the sum of the infrared signal data length and the image data length, and the sum of the infrared signal data length and the image data length is not equal to the effective data length, the first target data frame is a wrong frame.
S204, carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier recognition model, and determining whether an entity to be detected exists in the target carrier.
The descriptions of S201 to S202 and S204 may refer to the descriptions of S101 to S102 and S104, and the same technical effects can be achieved, which will not be described in detail.
Referring to fig. 3, fig. 3 is a flowchart of an embodiment of a method for detecting an entity in a carrier according to an embodiment of the present application. As shown in fig. 3, the following describes a method for detecting an entity in a carrier in a space application scenario according to an embodiment provided in the present application, including the following steps:
s301, accessing a data acquisition interface of external hardware equipment.
S302, acquiring an infrared induction level signal, wherein the infrared induction level signal is specifically used for acquiring the infrared induction level signal acquired through an infrared sensor and image data of a target carrier from a data acquisition interface of external hardware equipment.
S303, data frame reorganization, wherein the data frame reorganization is specifically used for reorganizing the infrared induction level signals and the image data according to a preset data frame format.
S304, judging whether image data exist, wherein the judging unit is specifically configured to judge whether the reorganized first target data frame value exists the image data, and specifically, judging whether the data identification frame in the first target data frame exists the image data.
And S305, if yes, inputting the image data into a trained carrier recognition model.
S306, determining whether an entity to be detected exists according to the output result.
And S307, performing database storage, wherein the database storage is specifically performed according to the result of judging whether the step S306 and the step S304 are negative.
And S308, data monitoring, wherein the data monitoring method is specifically used for carrying out real-time data monitoring on the entity data to be detected, and calling corresponding historical data from a database when a user wants to view the historical data.
Compared with the prior art, the method for detecting the entity in the carrier provided by the embodiment of the application judges whether the entity to be detected exists in the target carrier or not by combining the infrared induction level signals acquired by the infrared sensor and the image data and generating the first target data frame, realizes automatic detection of the entity to be detected in the target carrier, reduces labor cost and improves detection efficiency of the state of the entity to be detected.
In addition, the embodiment provided by the embodiment of the application effectively improves the preparation work before the launching of the space mission by monitoring the position and the state of the entity to be detected in real time.
Referring to fig. 4, fig. 4 is a schematic structural diagram of a device for detecting an entity in a carrier according to an embodiment of the present application. As shown in fig. 4, the detection device 400 of the entity in the carrier comprises:
the first determining module 410 is configured to determine whether a target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor.
Optionally, the types of the infrared sensing level signals include a high level signal and a low level signal, and the first determining module 410 is specifically configured to:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected;
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
The acquiring module 420 is configured to acquire image data of the target carrier if the image data exists.
The second determining module 430 is configured to reorganize the data frames of the infrared sensing level signal and the image data according to a preset data frame format, and determine a first target data frame of the target carrier.
Optionally, the second determining module 430 is specifically configured to:
and carrying out data frame recombination on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
The recognition module 440 is configured to perform data analysis on the first target data frame, input the analyzed image data into a trained carrier recognition model, and determine whether an entity to be detected exists in the target carrier 。
Compared with the prior art, the detection device 400 for the entity in the carrier provided by the embodiment of the application combines the infrared sensing level signal acquired by the infrared sensor with the image data to generate the first target data frame to judge whether the entity to be detected exists in the target carrier, so that the automatic detection of the entity to be detected in the target carrier is realized, the labor cost is reduced, and the detection efficiency of the state of the entity to be detected is improved.
In addition, the embodiment provided by the embodiment of the application effectively improves the preparation work before the launching of the space mission by monitoring the position and the state of the entity to be detected in real time.
Referring to fig. 5, fig. 5 is a second schematic structural diagram of a device for detecting an entity in a carrier according to an embodiment of the present disclosure. As shown in fig. 5, the detection device 400 of the entity in the carrier comprises:
the first determining module 410 is configured to determine whether a target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor.
Optionally, the types of the infrared sensing level signals include a high level signal and a low level signal, and the first determining module 410 is specifically configured to:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected;
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
The acquiring module 420 is configured to acquire image data of the target carrier if the image data exists.
The second determining module 430 is configured to reorganize the data frames of the infrared sensing level signal and the image data according to a preset data frame format, and determine a first target data frame of the target carrier.
Optionally, the second determining module 430 is specifically configured to:
and carrying out data frame recombination on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
The recognition module 440 is configured to perform data analysis on the first target data frame, input the analyzed image data into a trained carrier recognition model, and determine whether an entity to be detected exists in the target carrier 。
And a third determining module 450, configured to reorganize the data frames of the infrared sensing level signal according to a preset format frame type if the second target data frame of the target carrier does not exist.
And the display module 460 is configured to perform data analysis on the second target data frame, and store and display the analyzed infrared induction level signal.
Compared with the prior art, the detection device 400 for the entity in the carrier provided by the embodiment of the application combines the infrared sensing level signal acquired by the infrared sensor with the image data to generate the first target data frame to judge whether the entity to be detected exists in the target carrier, so that the automatic detection of the entity to be detected in the target carrier is realized, the labor cost is reduced, and the detection efficiency of the state of the entity to be detected is improved.
In addition, the embodiment provided by the embodiment of the application effectively improves the preparation work before the launching of the space mission by monitoring the position and the state of the entity to be detected in real time.
Referring to fig. 6, fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application. As shown in fig. 6, the electronic device 600 includes a processor 610, a memory 620, and a bus 630.
The memory 620 stores machine-readable instructions executable by the processor 610, when the electronic device 600 is running, the processor 610 communicates with the memory 620 through the bus 630, and when the machine-readable instructions are executed by the processor 610, the steps of the method for detecting an entity in a carrier in the method embodiments shown in fig. 1 and fig. 2 can be executed, and detailed description of the method embodiments will be omitted.
The embodiment of the present application further provides a computer readable storage medium, where a computer program is stored on the computer readable storage medium, and when the computer program is executed by a processor, the steps of the method for detecting an entity in a carrier in the method embodiments shown in the foregoing fig. 1 and fig. 2 may be executed, and detailed implementation manner may refer to the method embodiments and will not be repeated herein.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods may be implemented in other manners. The above-described apparatus embodiments are merely illustrative, for example, the division of the units is merely a logical function division, and there may be other manners of division in actual implementation, and for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some communication interface, device or unit indirect coupling or communication connection, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in each embodiment of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a non-volatile computer readable storage medium executable by a processor. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps 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 random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present application, and are intended to be included in the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. The method for detecting the entity in the carrier is characterized by comprising the following steps of:
determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor;
if so, acquiring the image data of the target bearing object;
carrying out data frame reorganization on the infrared induction level signals and the image data according to a preset data frame format, and determining a first target data frame of the target carrier;
and carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier recognition model, and determining whether an entity to be detected exists in the target carrier.
2. The method for detecting an entity in a carrier according to claim 1, wherein the types of the infrared sensing level signals include a high level signal and a low level signal, and the determining whether the target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor includes:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the area to be detected;
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
3. The method for detecting an entity in a carrier according to claim 1, wherein the step of reorganizing the infrared sensing level signal and the image data into data frames according to a predetermined data frame format, and determining a first target data frame of the target carrier includes:
and carrying out data frame recombination on the infrared induction level signal and the image data according to a preset mark, acquisition time, infrared induction level signal length and image data length, and determining a first target data frame of a target carrier.
4. The method for detecting an entity in a carrier according to claim 1, wherein after determining whether a target carrier exists in the area to be detected according to the type of the infrared sensing level signal collected by the infrared sensor, the method for detecting an entity in a carrier further comprises:
if the target load object does not exist, carrying out data frame recombination on the infrared induction level signal according to a preset format frame type, and determining a second target data frame of the target load object;
and carrying out data analysis on the second target data frame, and storing and displaying the analyzed infrared induction level signal.
5. The method of claim 1, wherein the trained carrier recognition model is determined by:
acquiring image data of a sample carrier and a label of the image data of the sample carrier, wherein the label is used for representing real type information of a sample entity in the sample carrier;
performing data enhancement processing on the image data of the sample carrier to determine enhanced image data of the carrier;
inputting the enhanced image data into an initial carrier recognition model for training, and determining preset type information of sample entities in the sample carrier;
and when the loss value between the preset type information of the sample entity and the real type information of the sample entity is smaller than a preset threshold value, training is stopped, and a trained carrier recognition model is determined.
6. The method of claim 5, wherein the performing data enhancement processing on the image data of the sample carrier to determine enhanced image data of the carrier comprises:
and carrying out translation, scaling, rotation, cutting, overturning and splicing processing on the image data of the sample carrier, and determining the enhanced image data of the carrier.
7. A device for detecting an entity in a carrier, the device comprising:
the first determining module is used for determining whether a target carrier exists in the region to be detected according to the type of the infrared induction level signal acquired by the infrared sensor;
the acquisition module is used for acquiring the image data of the target bearing object if the target bearing object exists;
the second determining module is used for carrying out data frame reorganization on the infrared induction level signal and the image data according to a preset data frame format to determine a first target data frame of the target carrier;
the identification module is used for carrying out data analysis on the first target data frame, inputting the analyzed image data into a trained carrier identification model and determining whether an entity to be detected exists in the target carrier.
8. The device for detecting an entity in a carrier according to claim 7, wherein the type of infrared induced level signal comprises a high level signal and a low level signal, and the first determining module is specifically configured to:
if the infrared induction level signal acquired by the infrared sensor is a high level signal, determining that no target carrier exists in the region to be detected:
if the infrared sensing level signal acquired by the infrared sensor is a low level signal, determining that a target carrier exists in the area to be detected.
9. An electronic device, comprising: a processor, a memory and a bus, said memory storing machine readable instructions executable by said processor, said processor and said memory communicating via said bus when the electronic device is running, said machine readable instructions being executed by said processor to perform the steps of the method of detecting an entity in a carrier as claimed in any one of claims 1-6.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the method of detecting an entity in a carrier according to any of claims 1-6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310126366.2A CN116188415A (en) | 2023-02-03 | 2023-02-03 | Method and device for detecting entity in carrier and electronic equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310126366.2A CN116188415A (en) | 2023-02-03 | 2023-02-03 | Method and device for detecting entity in carrier and electronic equipment |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116188415A true CN116188415A (en) | 2023-05-30 |
Family
ID=86432320
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310126366.2A Pending CN116188415A (en) | 2023-02-03 | 2023-02-03 | Method and device for detecting entity in carrier and electronic equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116188415A (en) |
-
2023
- 2023-02-03 CN CN202310126366.2A patent/CN116188415A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US20100201880A1 (en) | Shot size identifying apparatus and method, electronic apparatus, and computer program | |
| US10445590B2 (en) | Image processing apparatus and method and monitoring system | |
| CN104519316A (en) | Monitoring system, monitoring method, monitoring program, and recording medium | |
| US9836826B1 (en) | System and method for providing live imagery associated with map locations | |
| CN110572636B (en) | Camera contamination detection method and device, storage medium and electronic equipment | |
| CN104469127A (en) | Photographing method and photographing device | |
| US6819353B2 (en) | Multiple backgrounds | |
| JP2007300531A (en) | Object detector | |
| CN112333467A (en) | Method, system, and medium for detecting keyframes of a video | |
| US20200250401A1 (en) | Computer system and computer-readable storage medium | |
| CN112422909B (en) | Video behavior analysis management system based on artificial intelligence | |
| US20100296742A1 (en) | System and method for object based post event forensics in video surveillance systems | |
| JP2005056213A5 (en) | ||
| KR101395666B1 (en) | Surveillance apparatus and method using change of video image | |
| CN113408479A (en) | Flame detection method and device, computer equipment and storage medium | |
| CN116188415A (en) | Method and device for detecting entity in carrier and electronic equipment | |
| CN111753587B (en) | Ground falling detection method and device | |
| CN110084076A (en) | Evidence display methods and device | |
| CN115278217A (en) | Image picture detection method and device, electronic equipment and storage medium | |
| CN113625864B (en) | Virtual scene display method, system, equipment and storage medium based on Internet of things | |
| US20110234912A1 (en) | Image activity detection method and apparatus | |
| KR101984069B1 (en) | Image based intelligent vibration monitoring method | |
| CN109034067B (en) | Method, system, equipment and storage medium for commodity image reproduction detection | |
| CN111881787A (en) | Camera-based store illegal operation behavior identification method and system | |
| CN111488846A (en) | Method and equipment for identifying water level |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |