CN115311814A - Dangerous area person identification early warning system and method based on machine vision - Google Patents
Dangerous area person identification early warning system and method based on machine vision Download PDFInfo
- Publication number
- CN115311814A CN115311814A CN202210476512.XA CN202210476512A CN115311814A CN 115311814 A CN115311814 A CN 115311814A CN 202210476512 A CN202210476512 A CN 202210476512A CN 115311814 A CN115311814 A CN 115311814A
- Authority
- CN
- China
- Prior art keywords
- early warning
- picture information
- information
- warning system
- personnel identification
- 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
- 238000000034 method Methods 0.000 title claims abstract description 27
- 230000005641 tunneling Effects 0.000 claims abstract description 36
- 238000007781 pre-processing Methods 0.000 claims abstract description 17
- 231100001261 hazardous Toxicity 0.000 claims description 9
- 238000012549 training Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 238000012545 processing Methods 0.000 claims description 6
- 230000008569 process Effects 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 3
- 230000009471 action Effects 0.000 claims description 2
- 230000003993 interaction Effects 0.000 claims description 2
- 238000010801 machine learning Methods 0.000 claims description 2
- 230000003542 behavioural effect Effects 0.000 claims 1
- 239000003245 coal Substances 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000006870 function Effects 0.000 description 5
- 238000010586 diagram Methods 0.000 description 4
- 238000004519 manufacturing process Methods 0.000 description 4
- 238000012360 testing method Methods 0.000 description 3
- 238000009412 basement excavation Methods 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000005065 mining Methods 0.000 description 2
- 206010063385 Intellectualisation Diseases 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000006399 behavior Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/19—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems
- G08B13/191—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using infrared-radiation detection systems using pyroelectric sensor means
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B13/00—Burglar, theft or intruder alarms
- G08B13/18—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength
- G08B13/189—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems
- G08B13/194—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems
- G08B13/196—Actuation by interference with heat, light, or radiation of shorter wavelength; Actuation by intruding sources of heat, light, or radiation of shorter wavelength using passive radiation detection systems using image scanning and comparing systems using television cameras
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B31/00—Predictive alarm systems characterised by extrapolation or other computation using updated historic data
Landscapes
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Emergency Management (AREA)
- Business, Economics & Management (AREA)
- Health & Medical Sciences (AREA)
- Computing Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- General Health & Medical Sciences (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Alarm Systems (AREA)
Abstract
The application provides a dangerous area personnel identification early warning system and method based on machine vision, the system includes: the system comprises a camera, an industrial personal computer and a display screen; the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer; the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information; and the display screen is used for displaying the running track prejudgment information of the working personnel. The technical scheme provided by the application ensures the safety of the working personnel of the tunneling working face and effectively improves the intelligent level of the tunneling working face.
Description
Technical Field
The application relates to the technical field of personnel identification and early warning, in particular to a dangerous area personnel identification and early warning system and method based on machine vision.
Background
At present, tunneling is an important production link in coal mine production, and mining is repeated and tunneling is performed first. The rapid excavation of the coal mine tunnel is a key technical measure for ensuring high and stable yield of a mine in a coal mine, and the excavation technology and the equipment level thereof are directly related to the capacity and safety of coal mine production. The high-efficiency safety guarantee is a necessary condition for ensuring high yield and high efficiency of a mine and is a development direction of a roadway tunneling technology. With the development of the fully mechanized mining technology, millions of tons of super working faces and even tens of millions of tons of super working faces are produced in China, and the number of roadways is greatly increased, so that roadway tunneling becomes a common and key technology for efficient and intensive production of coal mines.
Most of the heading faces in China have the conditions that a plurality of large-scale devices are frequently moved and the devices and workers alternately operate, the intelligent level is low, and great challenges are brought to field safety management work.
Disclosure of Invention
The application provides a dangerous area personnel identification early warning system and method based on machine vision to solve the technical problems that the intellectualization level is low and the safety of workers cannot be guaranteed in the related technology at least.
The embodiment of the first aspect of this application provides a dangerous area personnel discernment early warning system based on machine vision, dangerous area personnel discernment early warning system includes: the system comprises a camera, an industrial personal computer and a display screen;
the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer;
the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information;
and the display screen is used for displaying the running track prejudgment information of the working personnel.
The embodiment of the second aspect of the application provides a dangerous area person identification and early warning method based on machine vision, and the method comprises the following steps:
collecting picture information of a driving face;
preprocessing the picture information, and identifying workers in the tunneling working face based on the preprocessed picture information;
tracking the running track of a worker and prejudging to obtain running track prejudging information of the worker;
and early warning the operation track of the worker based on the prejudgment information.
An embodiment of a third aspect of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the program, implementing the method as described in the second aspect above.
A computer-readable storage medium is proposed in an embodiment of the fourth aspect of the present application, on which a computer program is stored, which, when executed by a processor, implements the method as described in the second aspect above.
The technical scheme provided by the embodiment of the application at least has the following beneficial effects:
the application provides a dangerous area personnel discernment early warning system and method based on machine vision, wherein, dangerous area personnel discernment early warning system includes: the system comprises a camera, an industrial personal computer and a display screen; the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer; the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information; and the display screen is used for displaying the operation track prejudgment information of the working personnel. The technical scheme provided by the application ensures the safety of the workers of the tunneling working face and effectively improves the intelligent level of the tunneling working face.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
fig. 1 is a block diagram of a dangerous area personnel identification and early warning system based on machine vision according to an embodiment of the present application;
FIG. 2 is another block diagram of a system for identifying and warning people in a dangerous area based on machine vision according to an embodiment of the present application;
fig. 3 is a structural diagram of a worker controller in a dangerous area personnel identification early warning system based on machine vision according to an embodiment of the present application;
fig. 4 is a flowchart of a dangerous area person identification and early warning method based on machine vision according to an embodiment of the present application;
description of the reference numerals:
the system comprises a camera 1, an industrial personal computer 2, a display screen 3, a vision box 4, an image preprocessing module 2-1, a personnel identification module 2-2 and a tracking early warning module 2-3.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the accompanying drawings are illustrative and intended to explain the present application and should not be construed as limiting the present application.
The application provides a dangerous area personnel discernment early warning system and method based on machine vision, wherein, dangerous area personnel discernment early warning system includes: the system comprises a camera, an industrial personal computer and a display screen; the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer; the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information; and the display screen is used for displaying the operation track prejudgment information of the working personnel. The technical scheme provided by the application ensures the safety of the working personnel of the tunneling working face and effectively improves the intelligent level of the tunneling working face.
The following describes a dangerous area person identification early warning system and method based on machine vision according to an embodiment of the present application with reference to the accompanying drawings.
Example one
Fig. 1 is a block diagram of a hazardous area personnel identification and early warning system based on machine vision according to an embodiment of the present application, and as shown in fig. 1, the hazardous area personnel identification and early warning system may include: the system comprises a camera 1, an industrial personal computer 2 and a display screen 3;
the camera 1 is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer 2;
the industrial personal computer 2 is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information;
and the display screen 3 is used for displaying the running track prejudgment information of the working personnel.
In the embodiment of the present disclosure, as shown in fig. 2, the system for identifying and warning people in a dangerous area further includes: a vision box 4;
the vision box 4 covers the outer side of the industrial personal computer 2 and is used for protecting the industrial personal computer 2 from being corroded by the severe environment of the driving working face.
In the embodiment of the disclosure, as shown in fig. 3, an image preprocessing module 2-1, a personnel identification module 2-2 and a tracking early warning module 2-3 are arranged in the industrial personal computer 2;
the image preprocessing module 2-1 is used for performing definition enhancement processing on the picture information;
a pre-trained personnel identification model is arranged in the personnel identification module 2-2 and is used for inputting the pre-processed picture information into the pre-trained personnel identification model and then identifying the working personnel tunneling the working face in the picture information;
the tracking early warning module 2-3 is provided with a pre-established motion model and an early warning boundary line, and is used for tracking the operation track of the worker, pre-judging the operation action of the worker at the next moment to obtain pre-judgment information corresponding to the operation track of the worker, then determining whether the operation track of the worker needs to be early warned according to the pre-judgment information and the early warning boundary line, and sending an early warning signal to the worker when the operation track of the worker needs to be early warned.
It should be noted that the early warning boundary line is defined on an upper computer in the industrial personal computer 2 by using an infrared thermal imager.
Further, the image preprocessing module 2-1 is specifically configured to:
and performing brightness enhancement processing on the picture information or performing image enhancement processing on the picture information based on a single-scale Retinex algorithm.
The person identification model 2-2 is obtained by training the initial YOLOV3 model based on training sample data formed by image information of the heading face having the worker mark information.
Illustratively, picture information of a tunneling working face in a historical period is acquired, 80% of the picture information is marked with personnel information to form training sample data, and 20% of the pictures are preprocessed to form test sample data;
carrying out optimization training on the initial Yolov3 model by using training sample data, adjusting model parameters until the error of a loss function is smaller than a set value, outputting a positioning identification result of a worker, testing the identification accuracy of the model by using test sample data, and continuously adjusting the model parameters; the mean square error loss MSE loss function is used in the training of the model, and the AdamaOptimizer is adopted in the optimizer.
In an embodiment of the present disclosure, the process of establishing the motion model includes:
establishing an initial motion model based on the motion trail of the working personnel in the historical period of the tunneling working face;
and learning the initial motion model according to a preset motion rule and a machine learning method to obtain a well-learned motion model.
Wherein the preset motion rule comprises:
behavior inference rule, interaction rule with others and influence rule of surrounding environment
Exemplary, the prediction of downhole pedestrian trajectories based on GAN includes: by combining the sequence prediction model and the GAN countermeasure network, the countermeasure idea is introduced into the task of pedestrian trajectory prediction, and the sample space of all possible solutions is shown.
In summary, the present invention provides a dangerous area personnel identification and early warning system based on machine vision, including: the system comprises a camera, an industrial personal computer and a display screen; the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer; the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information; the display screen is used for displaying the operation track prejudgment information of the workers, the safety of the workers on the tunneling working face is guaranteed, and the intelligent level of the tunneling working face is effectively improved.
Example two
Fig. 4 is a flowchart of a method for identifying and warning people in a dangerous area based on machine vision according to an embodiment of the present application, where as shown in fig. 4, the method may include:
step 1: collecting picture information of a driving face;
and 2, step: preprocessing the picture information, and identifying workers in the tunneling working face based on the preprocessed picture information;
and step 3: tracking and pre-judging the running track of a worker to obtain the running track pre-judging information of the worker;
and 4, step 4: and early warning the operation track of the worker based on the prejudgment information.
In conclusion, the dangerous area personnel identification and early warning method based on machine vision guarantees the safety of the tunneling working face workers and effectively improves the intelligent level of the tunneling working face.
Example 3
In order to implement the above embodiments, the present disclosure further provides an electronic device.
The electronic device provided by the embodiment comprises: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method of embodiment 2.
Example 4
In order to implement the above embodiments, the present disclosure also proposes a computer-readable storage medium.
The present embodiment provides a computer device having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method of embodiment 2.
In the description of the present specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing steps of a custom logic function or process, and alternate implementations are included within the scope of the preferred embodiment of the present application in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present application.
Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.
Claims (10)
1. The utility model provides a danger area personnel discernment early warning system based on machine vision, its characterized in that, danger area personnel discernment early warning system includes: the system comprises a camera, an industrial personal computer and a display screen;
the camera is used for collecting picture information of a tunneling working face and sending the picture information to the industrial personal computer;
the industrial personal computer is used for preprocessing the picture information, identifying workers in the tunneling working face based on the preprocessed picture information, tracking and prejudging the running track of the workers at the same time to obtain prejudgment information corresponding to the running track of the workers, and then carrying out early warning based on the prejudgment information;
and the display screen is used for displaying the operation track prejudgment information of the working personnel.
2. The hazardous area personnel identification and early warning system of claim 1, wherein the hazardous area personnel identification and early warning system further comprises: a vision box body;
the vision box body covers the outer side of the industrial personal computer and is used for protecting the industrial personal computer from being corroded by the severe environment of the tunneling working face.
3. The hazardous area personnel identification and early warning system of claim 1, wherein an image preprocessing module, a personnel identification module and a tracking and early warning module are arranged in the industrial personal computer;
the image preprocessing module is used for performing definition enhancement processing on the picture information;
a personnel identification module which is trained in advance is arranged in the personnel identification module and is used for inputting the preprocessed picture information into the personnel identification module which is trained in advance and then identifying the working personnel who tunnel the working face in the picture information;
the tracking and early warning module is provided with a pre-established motion model and an early warning boundary line, and is used for tracking the operation track of the staff, pre-judging the operation action of the staff at the next moment to obtain pre-judgment information corresponding to the operation track of the staff, then determining whether the operation track of the staff needs to be early warned according to the pre-judgment information and the early warning boundary line, and sending an early warning signal to the staff when the operation track of the staff needs to be early warned.
4. The hazardous area personnel identification and early warning system of claim 3, wherein the image preprocessing module is specifically configured to:
and performing brightness enhancement processing on the picture information or performing image enhancement processing on the picture information based on a single-scale Retinex algorithm.
5. The hazardous area personnel identification and early warning system of claim 3, wherein the personnel identification model is obtained by training an initial Yolov3 model based on training sample data formed by picture information of a heading face with staff marking information.
6. The hazardous area personnel identification and early warning system of claim 3, wherein the motion model establishing process comprises:
establishing an initial motion model based on the motion trail of the working personnel in the historical period of the tunneling working face;
and learning the initial motion model according to a preset motion rule and a machine learning method to obtain a well-learned motion model.
7. The hazardous area personnel identification and early warning system of claim 3, wherein the preset motion rules comprise:
behavioral inference rules, interaction rules with others, and influence rules of the surrounding environment.
8. A danger area personnel identification and early warning method based on the machine vision based danger area personnel identification and early warning system of any one of the claims 1-7, wherein the method comprises the following steps:
collecting picture information of a driving face;
preprocessing the picture information, and identifying workers in the tunneling working face based on the preprocessed picture information;
tracking and pre-judging the running track of a worker to obtain the running track pre-judging information of the worker;
and early warning the operation track of the worker based on the prejudgment information.
9. An electronic device, comprising: memory, processor and computer program stored on the memory and executable on the processor, which when executed by the processor implements the method as claimed in claim 8.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method as claimed in claim 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210476512.XA CN115311814A (en) | 2022-04-29 | 2022-04-29 | Dangerous area person identification early warning system and method based on machine vision |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210476512.XA CN115311814A (en) | 2022-04-29 | 2022-04-29 | Dangerous area person identification early warning system and method based on machine vision |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115311814A true CN115311814A (en) | 2022-11-08 |
Family
ID=83855495
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210476512.XA Pending CN115311814A (en) | 2022-04-29 | 2022-04-29 | Dangerous area person identification early warning system and method based on machine vision |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115311814A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170318360A1 (en) * | 2016-05-02 | 2017-11-02 | Bao Tran | Smart device |
| US20180083833A1 (en) * | 2016-09-16 | 2018-03-22 | Oracle International Corporation | Method and system for performing context-aware prognoses for health analysis of monitored systems |
| CN110225301A (en) * | 2019-05-14 | 2019-09-10 | 广西科飞科技发展有限公司 | A kind of personnel's Trajectory Tracking System based on security system |
| CN112154492A (en) * | 2018-03-19 | 2020-12-29 | 德尔克股份有限公司 | Early warning and collision avoidance |
| CN112879096A (en) * | 2021-03-09 | 2021-06-01 | 辽宁瑞华实业集团高新科技有限公司 | Heading work early warning system |
| CN113269008A (en) * | 2020-02-14 | 2021-08-17 | 宁波吉利汽车研究开发有限公司 | Pedestrian trajectory prediction method and device, electronic equipment and storage medium |
| US20210339772A1 (en) * | 2018-10-16 | 2021-11-04 | Five Al Limited | Driving scenarios for autonomous vehicles |
| CN113781527A (en) * | 2021-11-10 | 2021-12-10 | 华中科技大学 | Pedestrian trajectory prediction method and system based on multi-interaction space-time diagram network |
-
2022
- 2022-04-29 CN CN202210476512.XA patent/CN115311814A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170318360A1 (en) * | 2016-05-02 | 2017-11-02 | Bao Tran | Smart device |
| US20180083833A1 (en) * | 2016-09-16 | 2018-03-22 | Oracle International Corporation | Method and system for performing context-aware prognoses for health analysis of monitored systems |
| CN112154492A (en) * | 2018-03-19 | 2020-12-29 | 德尔克股份有限公司 | Early warning and collision avoidance |
| US20210339772A1 (en) * | 2018-10-16 | 2021-11-04 | Five Al Limited | Driving scenarios for autonomous vehicles |
| CN110225301A (en) * | 2019-05-14 | 2019-09-10 | 广西科飞科技发展有限公司 | A kind of personnel's Trajectory Tracking System based on security system |
| CN113269008A (en) * | 2020-02-14 | 2021-08-17 | 宁波吉利汽车研究开发有限公司 | Pedestrian trajectory prediction method and device, electronic equipment and storage medium |
| CN112879096A (en) * | 2021-03-09 | 2021-06-01 | 辽宁瑞华实业集团高新科技有限公司 | Heading work early warning system |
| CN113781527A (en) * | 2021-11-10 | 2021-12-10 | 华中科技大学 | Pedestrian trajectory prediction method and system based on multi-interaction space-time diagram network |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111144263B (en) | Construction worker high-falling accident early warning method and device | |
| CN114673558B (en) | Coal mine driving face risk identification and intelligent pre-control system and method | |
| US7464063B2 (en) | Information processor, state judging unit and diagnostic unit, information processing method, state judging method and diagnosing method | |
| CN108431585A (en) | Information processing unit and information processing method | |
| CN101908214B (en) | Moving object detection method with background reconstruction based on neighborhood correlation | |
| CN103065494B (en) | Free parking space detection method based on computer vision | |
| CN105095829A (en) | Face recognition method and system | |
| CN104778446A (en) | Method for constructing image quality evaluation and face recognition efficiency relation model | |
| CN113344882A (en) | Method for counting drill rods of underground drilling machine based on computer vision | |
| CN113569682A (en) | Video monitoring method and device for intelligently capturing mine identification elements | |
| WO2023104557A1 (en) | Machine-learning for safety rule violation determination | |
| CN116229299A (en) | Unmanned aerial vehicle-based building or structure damage assessment method, terminal and medium | |
| CN115311814A (en) | Dangerous area person identification early warning system and method based on machine vision | |
| Ji et al. | Bucket teeth detection based on faster region convolutional neural network | |
| CN116579601A (en) | Mine safety production risk monitoring and early warning system and method | |
| CN115423777A (en) | Roadway defect positioning method and device, readable storage medium and engineering equipment | |
| CN101984452B (en) | Method for predicting visual attention area transfer in gray images | |
| Kim et al. | On-site safety management using image processing and fuzzy inference | |
| CN113014894A (en) | Petroleum underground operation safety staring control method based on artificial intelligence | |
| CN112329601A (en) | Parking space detection method and device based on multi-task network | |
| CN111414502A (en) | Steel wire rope burr detection system based on block chain and BIM | |
| Liu et al. | Computer vision technologies and machine learning algorithms for construction safety management: A critical review | |
| EP4350639A1 (en) | Safety rule violation detection in a construction or constructed site | |
| CN105389806B (en) | One kind is caved in detection method and device | |
| CN104299002A (en) | Tower crane image detection method based on monitoring system |
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 | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20221116 Address after: Office Building, Block B, Shengbang Tianlong Building, Yiqi'a Town, Ordos City, Inner Mongolia Autonomous Region, 017200 Applicant after: CHINA COAL NORTHWEST ENERGY Co.,Ltd. Applicant after: UXIN BANNER MENGDA MINING CO.,LTD. Applicant after: SHANXI TIANDI COAL MINING MACHINERY Co.,Ltd. Address before: Office Building, Block B, Shengbang Tianlong Building, Yiqi'a Town, Ordos City, Inner Mongolia Autonomous Region, 017200 Applicant before: CHINA COAL NORTHWEST ENERGY Co.,Ltd. Applicant before: SHANXI TIANDI COAL MINING MACHINERY Co.,Ltd. |
|
| TA01 | Transfer of patent application right |