CN112863100B - Intelligent construction safety monitoring system and method - Google Patents
Intelligent construction safety monitoring system and method Download PDFInfo
- Publication number
- CN112863100B CN112863100B CN202011638201.6A CN202011638201A CN112863100B CN 112863100 B CN112863100 B CN 112863100B CN 202011638201 A CN202011638201 A CN 202011638201A CN 112863100 B CN112863100 B CN 112863100B
- Authority
- CN
- China
- Prior art keywords
- video
- construction
- intelligent
- module
- neural network
- 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.)
- Active
Links
Images
Classifications
-
- 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
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/02—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP]
- H04L67/025—Protocols based on web technology, e.g. hypertext transfer protocol [HTTP] for remote control or remote monitoring of applications
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Alarm Systems (AREA)
- Emergency Alarm Devices (AREA)
Abstract
The invention belongs to the field of construction safety monitoring, and particularly relates to an intelligent construction safety monitoring system and method. The intelligent construction safety monitoring system comprises a video image acquisition module, an intelligent chip and a monitoring module, wherein the video image acquisition module is used for acquiring a video image of a construction area and transmitting the video image to the intelligent chip; the intelligent chip carries out regional intrusion detection based on the video image of the construction region and detects the regional intrusion detection result; the multi-gas detection sensor is used for detecting gas in a construction site, the environment sensor is used for detecting environment information of a construction area, and the vibration sensor is used for detecting collision conditions at a fence of the construction area; the background server is used for receiving the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judging whether the alarm condition is met or not, and outputting corresponding alarm information; and the alarm terminal is used for receiving the alarm information and giving an alarm in time.
Description
Technical Field
The invention belongs to the field of construction safety monitoring, and particularly relates to an intelligent construction safety monitoring system and method.
Background
The statements in this section merely provide background information related to the present disclosure and may not constitute prior art.
Most of the current construction safety monitoring is carried out by installing a camera on site, and a more advanced construction safety monitoring method and a more advanced construction safety monitoring device are characterized in that a plurality of peripheral monitoring devices are arranged around a construction area, wherein the peripheral monitoring devices comprise a visible light camera and an infrared camera; after the pose of the peripheral monitoring device is initialized, the construction area is aligned, the picture is sent to the management platform, the management platform utilizes the neural network to analyze, and when abnormal personnel or an invading object enters the high-risk area, the neural network is used for identifying and alarming.
In the prior art, a management platform is used for processing pictures, and when the area monitored by the management platform is more than one construction area, video images of all the construction areas are transmitted to the management platform, so that the data processing load burden of the management platform is increased, and the timeliness of processing is influenced; and the video images of all the construction areas are transmitted to the management platform, and the condition of video image data concurrency can be generated, so that the receiving speed of the management platform is influenced, the management platform cannot respond in time, and finally the alarm cannot be given in time, so that the safety of the construction areas is influenced.
Disclosure of Invention
In order to solve at least one technical problem in the background art, the invention provides an intelligent construction safety monitoring system and method, which can relieve the burden of a background server, monitor a construction area in time and guarantee the safety of the construction area.
In order to achieve the purpose, the invention adopts the following technical scheme:
the invention provides an intelligent construction safety monitoring system in a first aspect.
An intelligent construction safety monitoring system, comprising:
the front-end intelligent construction safety monitoring device comprises a video image acquisition module, an intelligent chip, a multi-gas detection sensor, an environment sensor and a vibration sensor; the video image acquisition module is used for acquiring a video image of a construction area and transmitting the video image to the intelligent chip; the intelligent chip carries out regional intrusion detection based on the video image of the construction region and detects the regional intrusion detection result; the system comprises a plurality of gas detection sensors, an environment sensor, a vibration sensor and a control module, wherein the plurality of gas detection sensors are used for detecting gas in a construction site, the environment sensor is used for detecting environment information of a construction area, and the vibration sensor is used for detecting collision conditions at a fence of the construction area;
the background server is used for receiving the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judging whether the alarm condition is met or not, and outputting corresponding alarm information;
and the alarm terminal is used for receiving the alarm information and giving an alarm in time.
The intelligent chip is composed of an integrated video input module, a video processing subsystem module, an intelligent video engine module, a neural network acceleration engine module and a video graphics subsystem module.
In one embodiment, the intelligent chip initializes the video input module, the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module during the starting process.
In one embodiment, during the initialization operation, the initialization of the neural network acceleration engine module includes loading a trained neural network model in a specific format.
As an embodiment, the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module perform multi-thread parallel operation; the VitoVo thread operation is carried out among the video processing subsystem module, the neural network acceleration engine module and the video graphics subsystem module; and a detect thread operation is carried out between the intelligent video engine module and the neural network acceleration engine module.
In a Vitevo thread operation, as an embodiment, frame data is extracted from the extended video frame data and placed in a frame data link list; in detect thread operation, taking out frame data from the frame data linked list in sequence, judging whether the frame data is identified, defining a zone bit according to the result of identifying, and storing the zone bit and the frame number into the zone bit linked list.
In the Vitevo thread operation, the identification result and the frame number of the intrusion object of the frame data are sequentially extracted from the identification result linked list, and the identification result and the frame number of the intrusion object of the frame data are identified by the neural network acceleration engine module in the detection thread and are stored in the identification result linked list.
In one embodiment, the video processing subsystem module is configured to decompose video data into base video data and extended video data.
In one embodiment, the intelligent video engine module is configured to convert image frame data in the current extended video data into frame data in an image format matched with the neural network model.
The second aspect of the invention provides an intelligent construction safety monitoring method.
A monitoring method adopting the intelligent construction safety monitoring system comprises the following steps:
the front-end intelligent construction safety monitoring device collects video images of a construction area, carries out area intrusion detection based on the video images of the construction area, and detects the area intrusion detection result; detecting gas in a construction site, detecting environmental information of a construction area, and detecting collision conditions at a fence of the construction area;
the background server receives the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judges whether the alarm condition is met, and outputs corresponding alarm information;
and the alarm terminal receives the alarm information and gives an alarm in time.
Compared with the prior art, the invention has the beneficial effects that:
the system monitors the construction engineering site in real time, timely learns the actual conditions in the construction process, masters the potential safety hazards, timely makes adjustment measures, improves the construction scheme according to the actual conditions and can ensure the smooth construction. During construction, the system can provide real and objective information for the construction safety of a construction unit, can find potential safety hazards in time, is convenient to process in time, and can really realize dynamic construction.
According to the invention, the video image of the construction area is processed by using the front-end intelligent construction safety monitoring device, the area intrusion detection result can be obtained in real time, the problem that the background server cannot respond in time due to the concurrence of the video image is avoided, a large amount of image processing work of the background server is shared, the real-time performance of construction area monitoring is improved, and the safety of the construction area is ensured;
the invention utilizes the front-end intelligent construction safety monitoring device to detect gas in a construction site, detect environmental information of a construction area and detect the collision condition of a fence of the construction area, and the information is transmitted to the background server, thereby reducing the burden of the background server, and monitoring the construction area at multiple angles to ensure the safety of the construction area.
Advantages of additional aspects of the invention 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 invention.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, are included to provide a further understanding of the invention, and are incorporated in and constitute a part of this specification, illustrate exemplary embodiments of the invention and together with the description serve to explain the invention and not to limit the invention.
Fig. 1 is a schematic structural diagram of an intelligent construction safety monitoring system according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and examples.
It is to be understood that the following detailed description is exemplary and is intended to provide further explanation of the invention as claimed. 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 invention belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the invention. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
As shown in fig. 1, the intelligent construction safety monitoring system of the embodiment includes:
(1) the front-end intelligent construction safety monitoring device comprises a video image acquisition module, an intelligent chip, a multi-gas detection sensor, an environment sensor and a vibration sensor; the video image acquisition module is used for acquiring a video image of a construction area and transmitting the video image to the intelligent chip; the intelligent chip carries out regional intrusion detection based on the video image of the construction region and detects the regional intrusion detection result; the multi-gas detection sensor is used for detecting gas of a construction site, the environment sensor is used for detecting environment information of a construction area, and the vibration sensor is used for detecting collision conditions of fences of the construction area.
In specific implementation, the front-end intelligent construction safety monitoring device is internally provided with an intelligent chip and adopts a starlight level 500 ten thousand pixel 1/2.8 inch CMOS image sensor, so that the low-illumination effect is good, and the image definition is high; the double code streams are supported, and H.265@ H.264 coding, ultra-low delay, ultra-low code rate and high compression ratio are met; the system supports real-time monitoring in 4 directions, cameras are arranged in the front, back, left and right directions, 4G wireless transmission is achieved, and the construction site can be fully controlled by a mobile phone anytime and anywhere; the regional intrusion detection and the collision detection are supported, and sound and light alarm is carried out by adopting an explosion flashing lamp, a high pitch horn and the like; the method supports monitoring of flammable, explosive, toxic and other dangerous gas leakage, and can fully guarantee the safety of construction sites, particularly tunnel construction; the collection of environmental parameters such as temperature, humidity, illumination intensity, rain and snow, wind speed and direction, noise, PM2.5/PM10 and the like is supported; the GPS/BD double-positioning module can acquire the position information of the equipment at any time; the lifting rod can lift as required by 1.8-2.5 meters in a hand-operated lifting rod mode, and can move freely by adopting a roller design; the solar energy collecting device adopts a 100W solar panel and 12/100AH lithium batteries, and can continuously work for more than 3 days in rainy and cloudy days when the power is full; has the functions of IP67 level water and dust prevention, etc.
The front-end intelligent construction safety monitoring device also has the functions of wind speed and wind direction detection; a built-in GPS/BD double positioning module acquires a position at any time; the temperature, humidity and rain and snow detection is supported; sound and light alarms such as a flashing light and a tweeter; detecting vehicle regional invasion and collision; collecting PM2.5/PM10 environmental parameters; the scene around the working vehicle can be checked through the mobile phone at any time and any place; the monitoring of flammable, explosive, toxic and other dangerous gas leakage is supported; and (5) detecting noise.
The system can be applied to construction sites such as road maintenance operation, highways and the like or construction operation vehicles, monitors invasion, collision, toxic and harmful gases, inflammable and explosive gases, fire, severe weather and the like of vehicle regions in real time, and adopts the flashing lamps and the tweeters to perform sound and light alarm, thereby fully ensuring the construction safety of the operation vehicles and site personnel. In addition, the user can check the field condition of the working vehicle at any time and any place through the mobile phone.
In specific implementation, the intelligent chip is composed of an integrated video input module, a video processing subsystem module, an intelligent video engine module, a neural network acceleration engine module and a video graphics subsystem module.
Specifically, the intelligent chip initializes the video input module, the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module in the starting process.
And during the initialization operation, the initialization of the neural network acceleration engine module comprises loading the trained neural network model in a specific format. Before loading, format conversion needs to be carried out on a neural network model trained in a computer in advance, and the neural network model is converted into a specific format which can be loaded by a neural network acceleration engine module, so that the efficiency of video image data processing is improved.
As a specific embodiment, the training process of the neural network model is as follows:
collecting pictures containing an intrusion object to be detected in different scenes, carrying out unification treatment on the pictures, and labeling the class of the intrusion object to be detected to form a sample set; the sample set is divided into a training set and a testing set;
selecting one sample (Ai Bi) of the training set; wherein Bi is data and Ai is a label;
sending the data into a network, and calculating the actual output Y of the network; at the moment, the weights in the network are random;
calculating an error D-Bi-Y (the difference between the predicted value Bi and the actual value Y);
adjusting a weight matrix W according to the error D;
the above process is repeated for each sample until the error does not exceed the specified range for the entire training set.
In the present embodiment, the scenes include, but are not limited to, day, night, rainy day, snowy day, foggy day, and the like.
The yolov3 neural network model is used in this embodiment. The Caffe framework performs model training. And the format is consistent with the deep learning framework cafe supported by the front-end chip and is converted into a format which can be supported by the neural network acceleration engine module.
It should be noted here that the neural network model may also be other existing network structures, and those skilled in the art can specifically select the neural network model according to actual situations, and the details are not described here.
Specifically, the video input module is used for receiving real-time video data.
For example: the video input module receives real-time video data shot by a camera through an MIPI (Mobile Industry Processor Interface), processes the received original video data, and realizes the acquisition of the video data.
Specifically, the video processing subsystem module is used for decomposing original video data into basic video data and extended video data. Wherein the base video data maintains the resolution of the original video data. The resolution of the extended video data is matched to a neural network model within the neural network acceleration engine module.
Therefore, the consistency with the original data can be guaranteed, the invasion object outline frame identified in the later stage can be restored to the original video data more accurately, the resolution ratio of the expanded video data can be matched with the neural network model, and a data basis is provided for the neural network model.
Specifically, the intelligent video engine module is used for converting image frame data in the current extended video data into frame data in an image format matched with the neural network model.
In this embodiment, the image frame data in the current extended video data is in yuv format, and the image format matched with the neural network model in this embodiment is in rgb format.
Specifically, the neural network acceleration engine module is configured to obtain the frame data after format conversion, and identify and obtain the category and the contour coordinate position information of the intrusion object through the neural network model.
Specifically, the video graphics subsystem module is used for acquiring basic video data, and then drawing a contour frame for identifying the invading object in the basic video data based on the type and contour coordinate position information of the invading object.
In specific implementation, the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module perform multi-thread parallel operation; wherein, VitoVo thread operation is carried out among the video processing subsystem module, the neural network acceleration engine module and the video graphics subsystem module; and a detect thread operation is carried out between the intelligent video engine module and the neural network acceleration engine module. The parallel thread operation can improve the processing efficiency of video data and ensure the real-time property of the identification of the intrusion object.
Wherein, the VitoVo thread: input to the output thread. detect thread: and identifying and detecting the intrusion object.
In the VitoVo thread:
after the video processing subsystem module decomposes the video data collected by the video input module into basic video data and extended video data, extracting frame data from the extended video frame data, and putting the frame data into a frame data linked list;
sequentially taking out the zone bits and the frame numbers of the frame data from the zone bit linked list, wherein the zone bits of the frame data are stored in the zone bit linked list in the identification detect thread;
the flag bit represents whether the frame data is used for the identification of the intrusion object; because the frame rate of the intrusion object identification in the neural network acceleration engine module is less than the sampling frame rate of the video input module for the video data, in the embodiment, a frame extraction identification mode is adopted, and a flag bit is used for marking whether the frame data is used for the intrusion object identification;
sequentially extracting the identification result and the frame number of the invading object of the frame data from the identification result linked list, wherein the invading object identification result and the frame number of the frame data are obtained by the neural network acceleration engine module in the identification detect thread and are stored in the identification result linked list; the identification result comprises the intrusion object category and the coordinate position information of four points of the outline;
and the video graphics subsystem module acquires basic video data, and draws a contour frame for identifying the invading object in the basic video data according to the contour four-point coordinate position information obtained by the neural network acceleration engine module.
Identifying a detect thread:
taking out frame data from the frame data linked list in sequence, judging whether the frame data is identified, defining a zone bit according to the result of identifying whether the frame data is identified, and storing the zone bit and the frame number into the zone bit linked list;
an intelligent video engine module is adopted to convert the frame data in the input image format into the frame data in the image format required by the model,
acquiring frame data after format conversion by adopting a neural network acceleration engine module, and identifying to obtain the category and contour coordinate position information of an invasive object through a neural network model; and storing the category of the invading object, the contour coordinate position information and the frame number into an identification result linked list.
In the embodiment, the flag bit is used for detecting the corresponding thread, so that the processing sequence of the video images in the corresponding thread is guaranteed, and the omission of the video images is avoided.
(2) And the background server is used for receiving the regional intrusion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judging whether the alarm condition is met, and outputting corresponding alarm information.
Specifically, the alarm conditions include that an intrusion object exists, harmful gas exists in a construction site, the environment of a construction area is in extreme weather (for example, set weather such as haze, rain and snow) and a collision condition exists at a fence of a work area, and if any one of the conditions is met, corresponding alarm information is output.
(3) And the alarm terminal is used for receiving the alarm information and giving an alarm in time.
The alarm terminal can be an alarm or a terminal device with an alarm function.
The monitoring method adopting the intelligent construction safety monitoring system comprises the following specific steps:
step 1: carrying out regional intrusion detection on the video image, and detecting the regional intrusion detection result; detecting gas in a construction site, detecting environmental information of a construction area, and detecting a collision condition at a fence of the construction area;
step 2: the background server receives the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judges whether the alarm condition is met, and outputs corresponding alarm information;
and step 3: and the alarm terminal receives the alarm information and gives an alarm in time.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of a hardware embodiment, a software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention has been described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic disk, an optical disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), or the like.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (6)
1. An intelligent construction safety monitoring system, which is characterized by comprising:
the front-end intelligent construction safety monitoring device comprises a video image acquisition module, an intelligent chip, a multi-gas detection sensor, an environment sensor and a vibration sensor; the video image acquisition module is used for acquiring a video image of a construction area and transmitting the video image to the intelligent chip; the intelligent chip carries out regional intrusion detection based on the video image of the construction region and obtains a regional intrusion detection result; the multi-gas detection sensor is used for detecting gas in a construction site, the environment sensor is used for detecting environment information of a construction area, and the vibration sensor is used for detecting collision conditions at a fence of the construction area;
the background server is used for receiving the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judging whether the alarm condition is met or not, and outputting corresponding alarm information;
the alarm terminal is used for receiving alarm information and giving an alarm in time;
the intelligent chip is composed of a video input module, a video processing subsystem module, an intelligent video engine module, a neural network acceleration engine module and a video graphics subsystem module which are integrated into a whole;
the video input module is used for receiving real-time video data, processing the received original video data and realizing the acquisition of the video data;
the video processing subsystem module is used for decomposing original video data into basic video data and extended video data; wherein the base video data maintains a resolution of the original video data; the resolution of the extended video data is matched with a neural network model in a neural network acceleration engine module;
the intelligent video engine module is used for converting image frame data in the current extended video data into frame data in an image format matched with the neural network model;
the neural network acceleration engine module is used for acquiring frame data after format conversion and identifying to obtain the category and contour coordinate position information of the invasive object through a neural network model;
the video graphics subsystem module is used for acquiring basic video data, and then drawing a contour frame for identifying the invading object in the basic video data based on the type and contour coordinate position information of the invading object;
the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module perform multi-thread parallel operation;
the VitoVo thread operation is carried out among the video processing subsystem module, the neural network acceleration engine module and the video graphics subsystem module; and a detect thread operation is carried out between the intelligent video engine module and the neural network acceleration engine module.
2. The intelligent construction safety monitoring system according to claim 1, wherein the intelligent chip initializes the video input module, the video processing subsystem module, the intelligent video engine module, the neural network acceleration engine module and the video graphics subsystem module during startup.
3. The intelligent construction safety monitoring system according to claim 2, wherein during the initialization operation, the initialization of the neural network acceleration engine module includes loading a trained neural network model in a specific format.
4. The intelligent construction safety monitoring system according to claim 1, wherein in a Vitevo thread operation, frame data is extracted from the extended video frame data and put into a frame data link list; in detect thread operation, taking out frame data from the frame data linked list in sequence, judging whether the frame data is identified, defining a zone bit according to the result of identifying, and storing the zone bit and the frame number into the zone bit linked list.
5. The intelligent construction safety monitoring system according to claim 1, wherein in the Vitevo thread operation, the identification result of the intrusion object and the frame number of the frame data are sequentially extracted from the identification result linked list, and the identification result of the intrusion object and the frame number of the frame data are identified by the neural network acceleration engine module in the identification detect thread and stored in the identification result linked list.
6. A monitoring method using the intelligent construction safety monitoring system according to any one of claims 1 to 5, comprising:
the front-end intelligent construction safety monitoring device collects video images of a construction area, carries out area intrusion detection based on the video images of the construction area and obtains an area intrusion detection result; detecting gas in a construction site, detecting environmental information of a construction area, and detecting collision conditions at a fence of the construction area;
the background server receives the regional invasion detection result, the construction site gas detection result, the construction region environment information and the collision condition at the construction region fence, judges whether the alarm condition is met, and outputs corresponding alarm information;
and the alarm terminal receives the alarm information and gives an alarm in time.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011638201.6A CN112863100B (en) | 2020-12-31 | 2020-12-31 | Intelligent construction safety monitoring system and method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011638201.6A CN112863100B (en) | 2020-12-31 | 2020-12-31 | Intelligent construction safety monitoring system and method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112863100A CN112863100A (en) | 2021-05-28 |
| CN112863100B true CN112863100B (en) | 2022-09-06 |
Family
ID=76001345
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011638201.6A Active CN112863100B (en) | 2020-12-31 | 2020-12-31 | Intelligent construction safety monitoring system and method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112863100B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113570803A (en) * | 2021-07-28 | 2021-10-29 | 东莞全芯物联科技有限公司 | AI intelligence alarm host computer based on speech recognition and image recognition technique |
| CN114279489A (en) * | 2021-11-15 | 2022-04-05 | 深圳供电局有限公司 | Construction site safety monitoring device and system |
| CN115190271B (en) * | 2022-06-28 | 2024-09-06 | 慧之安信息技术股份有限公司 | Coal mine development safety monitoring method based on edge calculation |
| CN115580705A (en) * | 2022-08-02 | 2023-01-06 | 同济大学 | Audio and video monitoring and alarming system for construction activities close to pipelines under roads |
| CN115239206B (en) * | 2022-09-21 | 2022-11-22 | 山东鲁中公路建设有限公司 | Highway construction site safety control system based on data analysis |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108898086A (en) * | 2018-06-20 | 2018-11-27 | 腾讯科技（深圳）有限公司 | Method of video image processing and device, computer-readable medium and electronic equipment |
| CN109410496A (en) * | 2018-10-25 | 2019-03-01 | 北京交通大学 | Attack early warning method, apparatus and electronic equipment |
| CN110021014A (en) * | 2019-03-29 | 2019-07-16 | 无锡祥生医疗科技股份有限公司 | Nerve fiber recognition methods, system and storage medium neural network based |
| CN110111515A (en) * | 2018-02-01 | 2019-08-09 | 广州弘度信息科技有限公司 | A kind of border intrusion detection method, apparatus, server and system |
| CN110119757A (en) * | 2019-03-28 | 2019-08-13 | 北京奇艺世纪科技有限公司 | Model training method, video category detection method, device, electronic equipment and computer-readable medium |
| CN110381298A (en) * | 2019-08-16 | 2019-10-25 | 深圳江行联加智能科技有限公司 | A kind of method, apparatus and system of tunnel video monitoring |
| CN110619747A (en) * | 2019-09-27 | 2019-12-27 | 山东奥邦交通设施工程有限公司 | Intelligent monitoring method and system for highway road |
| CN111144232A (en) * | 2019-12-09 | 2020-05-12 | 国网智能科技股份有限公司 | Transformer substation electronic fence monitoring method based on intelligent video monitoring, storage medium and equipment |
| CN111783642A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Image identification method and device, electronic equipment and storage medium |
| CN211877114U (en) * | 2020-05-26 | 2020-11-06 | 周中兴 | Construction site supervisory system based on 5G |
| WO2020235819A1 (en) * | 2019-05-17 | 2020-11-26 | Jeong Tae Woong | Image-based real-time intrusion detection method and surveillance camera using artificial intelligence |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101724658B1 (en) * | 2012-12-21 | 2017-04-10 | 한국전자통신연구원 | Human detecting apparatus and method |
-
2020
- 2020-12-31 CN CN202011638201.6A patent/CN112863100B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110111515A (en) * | 2018-02-01 | 2019-08-09 | 广州弘度信息科技有限公司 | A kind of border intrusion detection method, apparatus, server and system |
| CN108898086A (en) * | 2018-06-20 | 2018-11-27 | 腾讯科技（深圳）有限公司 | Method of video image processing and device, computer-readable medium and electronic equipment |
| CN109410496A (en) * | 2018-10-25 | 2019-03-01 | 北京交通大学 | Attack early warning method, apparatus and electronic equipment |
| CN110119757A (en) * | 2019-03-28 | 2019-08-13 | 北京奇艺世纪科技有限公司 | Model training method, video category detection method, device, electronic equipment and computer-readable medium |
| CN110021014A (en) * | 2019-03-29 | 2019-07-16 | 无锡祥生医疗科技股份有限公司 | Nerve fiber recognition methods, system and storage medium neural network based |
| WO2020235819A1 (en) * | 2019-05-17 | 2020-11-26 | Jeong Tae Woong | Image-based real-time intrusion detection method and surveillance camera using artificial intelligence |
| CN110381298A (en) * | 2019-08-16 | 2019-10-25 | 深圳江行联加智能科技有限公司 | A kind of method, apparatus and system of tunnel video monitoring |
| CN110619747A (en) * | 2019-09-27 | 2019-12-27 | 山东奥邦交通设施工程有限公司 | Intelligent monitoring method and system for highway road |
| CN111144232A (en) * | 2019-12-09 | 2020-05-12 | 国网智能科技股份有限公司 | Transformer substation electronic fence monitoring method based on intelligent video monitoring, storage medium and equipment |
| CN211877114U (en) * | 2020-05-26 | 2020-11-06 | 周中兴 | Construction site supervisory system based on 5G |
| CN111783642A (en) * | 2020-06-30 | 2020-10-16 | 北京百度网讯科技有限公司 | Image identification method and device, electronic equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112863100A (en) | 2021-05-28 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112863100B (en) | Intelligent construction safety monitoring system and method | |
| CN108109385B (en) | System and method for identifying and judging dangerous behaviors of power transmission line anti-external damage vehicle | |
| CN103069434B (en) | For the method and system of multi-mode video case index | |
| CN201936415U (en) | Automatic forest fire identification and alarm system | |
| CN111432182A (en) | Safety supervision method and system for oil discharge place of gas station | |
| CN105074789A (en) | Fire detection system | |
| CN111462451A (en) | Straw burning detection alarm system based on video information | |
| CN111446920A (en) | Photovoltaic power station monitoring method, device and system | |
| CN111383429A (en) | Method, system, device and storage medium for detecting dress of workers in construction site | |
| CN107729850B (en) | Internet of things outdoor advertisement monitoring and broadcasting system | |
| CN106412522A (en) | Video analysis detection method and system of object in indoor and outdoor environment | |
| CN112449147B (en) | Video cluster monitoring system of photovoltaic power station and image processing method thereof | |
| CN106454253A (en) | Method and system for detecting area wandering | |
| CN105187800A (en) | Detector with high definition module structure | |
| CN112309068B (en) | Forest fire early warning method based on deep learning | |
| CN103152558B (en) | Based on the intrusion detection method of scene Recognition | |
| CN110867046A (en) | Intelligent car washer video monitoring and early warning system based on cloud computing | |
| CN104811586A (en) | Scene change video intelligent analyzing method, device, network camera and monitoring system | |
| Zhang et al. | Transmission line abnormal target detection based on machine learning yolo v3 | |
| CN112671104A (en) | Transformer substation multidimensional scene control platform facing complex scene | |
| CN116846059A (en) | Edge detection system for power grid inspection and monitoring | |
| Luo | Research on fire detection based on YOLOv5 | |
| CN118196624A (en) | Intelligent safety helmet identification system and method for construction site | |
| CN116310922A (en) | Petrochemical plant area monitoring video risk identification method, system, electronic equipment and storage medium | |
| CN117768610A (en) | High-speed railway perimeter intrusion risk monitoring method and system based on multi-target recognition |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |