CN113179389A - System and method for identifying crane jib of power transmission line dangerous vehicle - Google Patents
System and method for identifying crane jib of power transmission line dangerous vehicle Download PDFInfo
- Publication number
- CN113179389A CN113179389A CN202110404887.0A CN202110404887A CN113179389A CN 113179389 A CN113179389 A CN 113179389A CN 202110404887 A CN202110404887 A CN 202110404887A CN 113179389 A CN113179389 A CN 113179389A
- Authority
- CN
- China
- Prior art keywords
- crane
- transmission line
- identification model
- power transmission
- module
- 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
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
- H04N7/181—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast for receiving images from a plurality of remote sources
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
-
- 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
- G06N3/045—Combinations of networks
-
- 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
- G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Computing Systems (AREA)
- Bioinformatics & Computational Biology (AREA)
- Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Evolutionary Biology (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Signal Processing (AREA)
- Image Analysis (AREA)
Abstract
The invention relates to the technical field of computer vision, in particular to a system and a method for identifying a dangerous vehicle crane of a power transmission line. The system comprises an image acquisition module, a crane identification model and an alarm module which are connected with a control module; the image acquisition module is used for acquiring video images within the dangerous source range of the power transmission line and outputting the video images to the control module; the video image processed by the control module is output to the crane identification model, and the crane identification model is used for identifying whether a crane enters the power transmission line hazard source range or not; the control module starts the alarm module to alarm when the crane identification model identifies that the crane enters the dangerous source range of the power transmission line; the method comprises the steps of training a convolutional neural network by using a plurality of pre-collected pictures containing a crane in a danger source range to obtain a crane identification model. The crane in the hazard source range can be rapidly identified and an alarm is given, so that a user is reminded to timely handle the crane, and safety accidents are avoided.
Description
Technical Field
The invention relates to the technical field of computer vision, in particular to a system and a method for identifying a crane jib of a power transmission line dangerous vehicle.
Background
The construction of a strong smart power grid and the creation of a safe and controllable new generation power system are the fixed requirements of power grid development, and currently, along with economic development, more and more high-voltage-level lines are arranged in a line channel, so that higher requirements are provided for preventing dangerous source hidden dangers of a power transmission line channel. In the actual construction process, because the operation violating the regulations, the safety consciousness is weak, the field supervision measures are insufficient, and the like, the safety accidents of casualties caused by the fact that the crane booms touch the power transmission lines occur sometimes, and certain loss is caused to the stable operation of the urban power grid. Therefore, it is important to implement a vision-based crane jib identification technology in the key area of the power transmission line.
Firstly, the traditional information management method based on manual monitoring needs to consume a great deal of cost. In addition, due to the existence of subjective factors, the error and the missing judgment of the video analysis of the crane operation of the power transmission line by the supervision personnel are easily caused. In order to avoid accident loss caused by accidental factors, a plurality of methods for identifying crane operation based on overhead transmission lines are successively provided. The initial identification algorithm such as edge detection, background difference and the like needs to extract edge characteristics of the acquired images of the inner crane jib of the power transmission line hazard source, and also needs to perform preprocessing links such as denoising and the like on the images with complex backgrounds; and the crane jib characteristic of manual design is mainly pixel level, and the change such as yardstick, illumination, texture does not possess stronger robustness and robustness. With the development of the field of computer vision, the target detection based on deep learning can realize the rapid and accurate extraction of image features, and the target is positioned in a rectangular frame visualization mode in real time. However, the shape and the action of the crane jib on a construction site are irregular, and the rectangular frame detection method cannot give the height control point information of the jib, so that the relative position of the jib height point and a power transmission line wire cannot be effectively judged, and the danger level of the early warning of the crane jib can be further judged. The source of danger is not of substantial help. Therefore, the invention provides a system and a method for identifying the crane jib of the power transmission line dangerous vehicle, the designed convolutional neural network model can not only carry out end-to-end learning on the depth characteristics of a crane video image, overcome the problems of multi-scale, deformation and the like of a target in a complex power transmission line construction scene, but also carry out example segmentation on the crane jib action, acquire the pixel profile of the jib on the basis of accurate detection and identify the height-control point information. The crane jib early warning system is based on real-time monitoring and early warning decision of the crane jib by the identification system, and compared with a common AI target identification algorithm, the highest point of the crane jib can be more accurately positioned, so that the early warning danger level of the jib is more effectively judged, the safe operation of the construction of a crane of a power transmission line is ensured, and the intelligent and lean power grid operation and maintenance are realized.
Disclosure of Invention
In order to solve the problems, the invention provides a system and a method for identifying a crane of a power transmission line dangerous vehicle, which can rapidly identify the crane in a dangerous source range by using a convolutional neural network and give an alarm to remind a user of handling in time, thereby avoiding the generation of safety accidents and preventing the influence of unexpected power failure on the normal production and life order of enterprises and common people.
In order to achieve the above purpose, the invention adopts a technical scheme that:
a power transmission line dangerous vehicle crane identification system comprises an image acquisition module, a crane identification model and an alarm module, wherein the image acquisition module, the crane identification model and the alarm module are connected with a control module; the image acquisition module is used for acquiring video images within the dangerous source range of the power transmission line and outputting the video images to the control module; the crane identification model is used for identifying whether a crane enters the power transmission line danger source range or not; the control module starts the alarm module to alarm when the crane identification model identifies that the crane enters the dangerous source range of the power transmission line; wherein the crane identification model is obtained by training a convolutional neural network by using a plurality of pre-collected images containing the crane within the range of the hazard source.
Furthermore, the image acquisition module is a monocular camera, and the monocular camera is arranged in the dangerous source range area.
Furthermore, the alarm module is a sound alarm module.
The invention also provides a power transmission line dangerous vehicle crane identification method based on the power transmission line dangerous vehicle crane identification system, which comprises the following steps: step 1, picture collection, namely collecting real-time video data in a dangerous source range of a power transmission line through a monocular camera; step 2, training a crane identification model, manually collecting a plurality of pictures containing the crane and not containing the crane in the hazard source range as training pictures, and training a neural network model by adopting the training pictures to obtain the crane identification model; step 3, outputting the real-time video data to the crane identification model after being processed by the control module, identifying whether the real-time video data contains a dangerous source crane by using the crane identification model, and outputting an identification result to the control module; and 4, when the identification result of the crane identification model obtained by the control module is that the real-time video data contains a picture of the crane, the control module controls an alarm module to give an alarm.
Further, the step 2 comprises the following steps: step 2.1, manually collecting a plurality of pictures containing cranes and not containing cranes in the range of the hazard source as training pictures to obtain a sample set S; 2.2, carrying out sample expansion treatment on pictures containing the crane in the sample set S, wherein the expansion treatment comprises random clockwise rotation and anticlockwise rotation by 1-10 degrees, and adding Gaussian random noise to obtain an expanded sample set S1; step 2.3, constructing a convolution module WBlock; step 2.4, forming a residual error module RBlock, wherein if the input of the module is x, the output is F (x)) + x, and the weight layer is WBlock in the step 2.3; and 2.5, constructing a network model, and performing model training by adopting a cross entropy loss function and a gradient descent algorithm to obtain a crane identification model.
Further, the crane identification model in the step 2.5 further performs a pruning algorithm; the pruning algorithm comprises the following steps: step a, cutting off the weight which is approximately equal to 0, and leading the connection between two neurons to be lost; b, deleting the neurons with the neuron outputs close to zero; and c, training once by using the training set after the whole network is trimmed, and updating the parameters of the current neural network.
Further, the step 3 comprises the following steps: step 3.1, acquiring video data in the range of the dangerous source in real time through an image acquisition module and outputting the video data to the control module; step 3.2, suppose that the video image read at the moment t is ftWherein a single pixel point is xt＝(rt，gt，bt) Obeying a mixture gaussian distribution probability density function:
where k is the distribution mode integral, η (x)t，μi，t，τi，t) Is the ith Gaussian distribution at time ti，tIs the mean value ofi，tFor the purpose of its covariance matrix,
is variance, I is identity matrix, wi，tWeight of ith Gaussian distribution at time t；
Step 3.3, for each pixel x at the time t +1t+1And comparing the current K models according to the following formula, and directly finding a distribution model matched with a new pixel value, namely the mean deviation of the distribution model and the model is within 2.5 sigma, namely:
|xt+1-μi，t|≤2.5σi，t
when the matched mode meets the background requirement, the pixel belongs to the background, otherwise, the pixel belongs to the foreground; if the number of foreground pixels exceeds the threshold th, executing the step 3.4, otherwise, continuously returning to the step 3.1; and 3.4, the control module inputs the video picture processed in the step 3.3 into the crane identification model for identification, and obtains an identification result.
Compared with the prior art, the technical scheme of the invention has the following advantages:
(1) according to the identification system and the identification method for the crane of the power transmission line dangerous vehicle, a user can quickly identify the crane in a dangerous source range by using the convolutional neural network and give an alarm, so that the user is reminded of handling in time, safety accidents are avoided, and the influence of unexpected power failure on the normal production and life order of enterprises and common people is prevented.
(2) The invention carries out quick foreground and background rough classification on the image by establishing the Gaussian model, and then carries out fine classification by adopting the pruned neural network, thereby improving the identification efficiency. The neural network designed by the invention combines the residual error network, increases the network width and improves the adaptability of the network to the scale.
(3) The invention can greatly reduce the pressure of maintenance personnel of the power transmission line, and can monitor the power transmission line area in real time through an intelligent algorithm, find out the target of the dangerous source in time and prevent the target from damaging the power transmission line.
Drawings
FIG. 1 is a block diagram of a system for identifying a vehicle crane in a power transmission line danger according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for identifying a vehicle crane in a power transmission line in danger according to an embodiment of the present invention;
FIG. 3 is a block diagram of a convolution module according to an embodiment of the present invention;
FIG. 4 is a block diagram of a residual error network module according to an embodiment of the present invention;
FIG. 5 is a block diagram of a convolutional neural network in accordance with an embodiment of the present invention;
fig. 6 is a flow chart of a pruning algorithm according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the present embodiment provides a system for identifying a crane of a power transmission line dangerous vehicle, which includes an image acquisition module connected to a control module, a crane identification model, and an alarm module; the image acquisition module is used for acquiring video images within the dangerous source range of the power transmission line and outputting the video images to the control module; the crane identification model is used for identifying whether a crane enters the power transmission line danger source range or not; the control module starts the alarm module to alarm when the crane identification model identifies that the crane enters the dangerous source range of the power transmission line; wherein the crane identification model is obtained by training a convolutional neural network by using a plurality of pre-collected images containing the crane within the range of the hazard source.
The image acquisition module is a monocular camera, and the monocular camera is arranged in the dangerous source range area. The alarm module is a sound alarm module.
As shown in fig. 2, a method for identifying a vehicle crane in power transmission line danger based on the above system for identifying a vehicle crane in power transmission line danger includes the following steps: step 1, picture collection, namely collecting real-time video data in a dangerous source range of a power transmission line through a monocular camera; step 2, training a crane identification model, manually collecting a plurality of pictures containing the crane and not containing the crane in the hazard source range as training pictures, and training a neural network model by adopting the training pictures to obtain the crane identification model; step 3, outputting the real-time video data to the crane identification model after being processed by the control module, identifying whether the real-time video data contains a dangerous source crane by using the crane identification model, and outputting an identification result to the control module; and 4, when the identification result of the crane identification model obtained by the control module is that the real-time video data contains a picture of the crane, the control module controls an alarm module to give an alarm.
As shown in fig. 3 to 5, the step 2 includes the steps of: step 2.1, manually collecting a plurality of pictures containing cranes and not containing cranes in the range of the hazard source as training pictures to obtain a sample set S; 2.2, carrying out sample expansion treatment on pictures containing the crane in the sample set S, wherein the expansion treatment comprises random clockwise rotation and anticlockwise rotation by 1-10 degrees, and adding Gaussian random noise to obtain an expanded sample set S1; step 2.3, constructing a convolution module WBlock; step 2.4, forming a residual error module RBlock, wherein if the input of the module is x, the output is F (x)) + x, and the weight layer is WBlock in the step 2.3; and 2.5, constructing a network model, and performing model training by adopting a cross entropy loss function and a gradient descent algorithm to obtain a crane identification model.
As shown in fig. 6, the crane identification model in step 2.5 also performs a pruning algorithm; the pruning algorithm comprises the following steps: step a, cutting off the weight which is approximately equal to 0, and leading the connection between two neurons to be lost; b, deleting the neurons with the neuron outputs close to zero; and c, training once by using the training set after the whole network is trimmed, and updating the parameters of the current neural network.
The step 3 comprises the following steps: step 3.1, acquiring video data in the range of the dangerous source in real time through an image acquisition module and outputting the video data to the control module; step 3.2, suppose the video image read at time tIs ftWherein a single pixel point is xt＝(rt，gt，bt) Obeying a mixture gaussian distribution probability density function:
where k is the distribution mode integral, η (x)t，μi，t，τi，t) Is the ith Gaussian distribution at time ti，tIs the mean value ofi，tFor the purpose of its covariance matrix,
is variance, I is identity matrix, wi，tThe weight of the ith Gaussian distribution at the time t;
step 3.3, for each pixel x at the time t +1t+1And comparing the current K models according to the following formula, and directly finding a distribution model matched with a new pixel value, namely the mean deviation of the distribution model and the model is within 2.5 sigma, namely:
|xt+1-μi，t|≤2.5σi，t
when the matched mode meets the background requirement, the pixel belongs to the background, otherwise, the pixel belongs to the foreground; if the number of foreground pixels exceeds the threshold th, executing the step 3.4, otherwise, continuously returning to the step 3.1; and 3.4, the control module inputs the video picture processed in the step 3.3 into the crane identification model for identification, and obtains an identification result.
According to the identification system and the identification method for the crane of the power transmission line dangerous vehicle, a user can quickly identify the crane in a dangerous source range by using the convolutional neural network and give an alarm, so that the user is reminded of handling in time, safety accidents are avoided, and the influence of unexpected power failure on the normal production and life order of enterprises and common people is prevented.
The invention carries out quick foreground and background rough classification on the image by establishing the Gaussian model, and then carries out fine classification by adopting the pruned neural network, thereby improving the identification efficiency. The neural network designed by the invention combines the residual error network, increases the network width and improves the adaptability of the network to the scale.
The invention can greatly reduce the pressure of maintenance personnel of the power transmission line, and can monitor the power transmission line area in real time through an intelligent algorithm, find out the target of the dangerous source in time and prevent the target from damaging the power transmission line.
The above description is only an exemplary embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent structures or equivalent processes that are transformed by the content of the present specification and the attached drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. The system for identifying the dangerous vehicle crane of the power transmission line is characterized by comprising an image acquisition module, a crane identification model and an alarm module, wherein the image acquisition module, the crane identification model and the alarm module are connected with a control module; the image acquisition module is used for acquiring video images within the dangerous source range of the power transmission line and outputting the video images to the control module; the crane identification model is used for identifying whether a crane enters the power transmission line danger source range or not; the control module starts the alarm module to alarm when the crane identification model identifies that the crane enters the dangerous source range of the power transmission line; wherein the crane identification model is obtained by training a convolutional neural network by using a plurality of pre-collected images containing the crane within the range of the hazard source.
2. The electric transmission line dangerous vehicle crane recognition system of claim 1, wherein the image acquisition module is a monocular camera, and the monocular camera is disposed in the dangerous source range area.
3. The system of claim 1, wherein the alarm module is an audible alarm module.
4. The transmission line dangerous vehicle crane identification method based on the transmission line dangerous vehicle crane identification system of any one of claims 1 to 3, characterized by comprising the steps of:
step 1, picture collection, namely collecting real-time video data in a dangerous source range of a power transmission line through a monocular camera; step 2, training a crane identification model, manually collecting a plurality of pictures containing the crane and not containing the crane in the hazard source range as training pictures, and training a neural network model by adopting the training pictures to obtain the crane identification model; step 3, outputting the real-time video data to the crane identification model after being processed by the control module, identifying whether the real-time video data contains a dangerous source crane by using the crane identification model, and outputting an identification result to the control module; and 4, when the identification result of the crane identification model obtained by the control module is that the real-time video data contains a picture of the crane, the control module controls an alarm module to give an alarm.
5. The method for identifying a vehicle crane in danger based on an electric transmission line according to claim 4, characterized in that the step 2 comprises the following steps: step 2.1, manually collecting a plurality of pictures containing cranes and not containing cranes in the range of the hazard source as training pictures to obtain a sample set S; 2.2, carrying out sample expansion treatment on pictures containing the crane in the sample set S, wherein the expansion treatment comprises random clockwise rotation and anticlockwise rotation by 1-10 degrees, and adding Gaussian random noise to obtain an expanded sample set S1; step 2.3, constructing a convolution module WBlock; step 2.4, forming a residual error module RBlock, wherein if the input of the module is x, the output is F (x)) + x, and the weightlayer is the WBlock in the step 2.3; and 2.5, constructing a network model, and performing model training by adopting a cross entropy loss function and a gradient descent algorithm to obtain a crane identification model.
6. The method for identifying a crane based on a dangerous vehicle on a power transmission line as claimed in claim 5, wherein the crane identification model in the step 2.5 is further subjected to a pruning algorithm; the pruning algorithm comprises the following steps: step a, cutting off the weight which is approximately equal to 0, and leading the connection between two neurons to be lost; b, deleting the neurons with the neuron outputs close to zero; and c, training once by using the training set after the whole network is trimmed, and updating the parameters of the current neural network.
7. The method for identifying a vehicle crane in danger based on an electric transmission line according to claim 4, characterized in that the step 3 comprises the following steps: step 3.1, acquiring video data in the range of the dangerous source in real time through an image acquisition module and outputting the video data to the control module; step 3.2, suppose that the video image read at the moment t is ftWherein a single pixel point is xt＝(rt，gt，bt) Obeying a mixture gaussian distribution probability density function:
where k is the distribution mode integral, η (x)t，μi，t，τi，t) Is the ith Gaussian distribution at time ti，tIs the mean value ofi，tFor the purpose of its covariance matrix,
is variance, I is identity matrix, wi，tThe weight of the ith Gaussian distribution at the time t;
step 3.3, for each pixel x at the time t +1t+1And comparing the current K models according to the following formula, and directly finding a distribution model matched with a new pixel value, namely the mean deviation of the distribution model and the model is within 2.5 sigma, namely:
|xt+1-μi，t|≤2.5σi，t
when the matched mode meets the background requirement, the pixel belongs to the background, otherwise, the pixel belongs to the foreground; if the number of foreground pixels exceeds the threshold th, executing the step 3.4, otherwise, continuously returning to the step 3.1;
and 3.4, the control module inputs the video picture processed in the step 3.3 into the crane identification model for identification, and obtains an identification result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110404887.0A CN113179389A (en) | 2021-04-15 | 2021-04-15 | System and method for identifying crane jib of power transmission line dangerous vehicle |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110404887.0A CN113179389A (en) | 2021-04-15 | 2021-04-15 | System and method for identifying crane jib of power transmission line dangerous vehicle |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113179389A true CN113179389A (en) | 2021-07-27 |
Family
ID=76923256
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110404887.0A Pending CN113179389A (en) | 2021-04-15 | 2021-04-15 | System and method for identifying crane jib of power transmission line dangerous vehicle |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113179389A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114612853A (en) * | 2022-02-11 | 2022-06-10 | 江苏濠汉信息技术有限公司 | Vehicle detection system and method based on attention mechanism and time sequence image analysis |
| CN114926755A (en) * | 2022-02-15 | 2022-08-19 | 江苏濠汉信息技术有限公司 | Dangerous vehicle detection system and method fusing neural network and time sequence image analysis |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108109385A (en) * | 2018-01-18 | 2018-06-01 | 南京杰迈视讯科技有限公司 | A kind of vehicle identification of power transmission line external force damage prevention and hazardous act judgement system and method |
| CN110060299A (en) * | 2019-04-18 | 2019-07-26 | 中国测绘科学研究院 | Danger source identifies and positions method in passway for transmitting electricity based on binocular vision technology |
| CN110807352A (en) * | 2019-08-29 | 2020-02-18 | 浙江零跑科技有限公司 | In-vehicle and out-vehicle scene visual analysis method for dangerous driving behavior early warning |
| CN111882810A (en) * | 2020-07-31 | 2020-11-03 | 广州市微智联科技有限公司 | Fire identification and early warning method and system |
| CN112132043A (en) * | 2020-09-24 | 2020-12-25 | 南京安元科技有限公司 | Fire fighting channel occupation self-adaptive detection method based on monitoring video |
-
2021
- 2021-04-15 CN CN202110404887.0A patent/CN113179389A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108109385A (en) * | 2018-01-18 | 2018-06-01 | 南京杰迈视讯科技有限公司 | A kind of vehicle identification of power transmission line external force damage prevention and hazardous act judgement system and method |
| CN110060299A (en) * | 2019-04-18 | 2019-07-26 | 中国测绘科学研究院 | Danger source identifies and positions method in passway for transmitting electricity based on binocular vision technology |
| CN110807352A (en) * | 2019-08-29 | 2020-02-18 | 浙江零跑科技有限公司 | In-vehicle and out-vehicle scene visual analysis method for dangerous driving behavior early warning |
| CN111882810A (en) * | 2020-07-31 | 2020-11-03 | 广州市微智联科技有限公司 | Fire identification and early warning method and system |
| CN112132043A (en) * | 2020-09-24 | 2020-12-25 | 南京安元科技有限公司 | Fire fighting channel occupation self-adaptive detection method based on monitoring video |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114612853A (en) * | 2022-02-11 | 2022-06-10 | 江苏濠汉信息技术有限公司 | Vehicle detection system and method based on attention mechanism and time sequence image analysis |
| CN114926755A (en) * | 2022-02-15 | 2022-08-19 | 江苏濠汉信息技术有限公司 | Dangerous vehicle detection system and method fusing neural network and time sequence image analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN106056079B (en) | A kind of occlusion detection method of image capture device and human face five-sense-organ | |
| CN109460699A (en) | A kind of pilot harness's wearing recognition methods based on deep learning | |
| CN107392112A (en) | A kind of facial expression recognizing method and its intelligent lock system of application | |
| CN111553214B (en) | Method and system for detecting smoking behavior of driver | |
| CN112149514B (en) | Method and system for detecting safety dressing of construction worker | |
| CN105678213B (en) | Dual-mode mask person event automatic detection method based on video feature statistics | |
| CN113179389A (en) | System and method for identifying crane jib of power transmission line dangerous vehicle | |
| CN108960185A (en) | Vehicle target detection method and system based on YOLOv2 | |
| CN111091110A (en) | Wearing identification method of reflective vest based on artificial intelligence | |
| CN115995056A (en) | Automatic bridge disease identification method based on deep learning | |
| CN117392733B (en) | Acne grading detection method and device, electronic equipment and storage medium | |
| CN112232235B (en) | Intelligent factory remote monitoring method and system based on 5G sum algorithm | |
| CN111860143A (en) | Real-time flame detection method for inspection robot | |
| CN115223249A (en) | Quick analysis and identification method for unsafe behaviors of underground personnel based on machine vision | |
| CN113255519A (en) | Crane lifting arm identification system and multi-target tracking method for power transmission line dangerous vehicle | |
| CN115410114A (en) | Urban rail flood prevention early warning method and system based on multiple characteristics | |
| CN108520539B (en) | Image target detection method based on sparse learning variable model | |
| CN105335688A (en) | Identification method of airplane model on the basis of visual image | |
| CN108734709A (en) | A kind of identification of insulator flange shape parameter and destructive test method | |
| CN116862871A (en) | Wood counting method based on mixed characteristics | |
| CN115909212A (en) | Real-time early warning method for typical violation behaviors of power operation | |
| CN114758294A (en) | Method, device, equipment and storage medium for monitoring dangerous behaviors of distribution center | |
| CN113989886A (en) | Crew identity verification method based on face recognition | |
| CN115035332B (en) | Antigen detection result authentication method based on image recognition | |
| CN115082865B (en) | Bridge crane intrusion dangerous behavior early warning method and system based on visual image 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 | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210727 |
|
| RJ01 | Rejection of invention patent application after publication |