CN113808405A - Real-time early warning method for muck truck - Google Patents
Real-time early warning method for muck truck Download PDFInfo
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- CN113808405A CN113808405A CN202010527393.7A CN202010527393A CN113808405A CN 113808405 A CN113808405 A CN 113808405A CN 202010527393 A CN202010527393 A CN 202010527393A CN 113808405 A CN113808405 A CN 113808405A
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- 238000000034 method Methods 0.000 title claims abstract description 27
- 238000012544 monitoring process Methods 0.000 claims abstract description 21
- 238000007619 statistical method Methods 0.000 claims abstract description 11
- 230000006399 behavior Effects 0.000 claims abstract description 10
- 239000002689 soil Substances 0.000 claims description 15
- 239000002699 waste material Substances 0.000 claims description 15
- 238000007689 inspection Methods 0.000 claims description 11
- 238000013135 deep learning Methods 0.000 claims description 10
- 238000001514 detection method Methods 0.000 claims description 8
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 239000002893 slag Substances 0.000 claims description 7
- 238000013527 convolutional neural network Methods 0.000 description 8
- 238000013528 artificial neural network Methods 0.000 description 4
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- G—PHYSICS
- G08—SIGNALLING
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- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
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Abstract
The invention relates to a real-time early warning method for a muck truck, which comprises the following steps: s1, acquiring video pictures in real time; s2, detecting and identifying the type and the license plate of the muck car in the picture; s3, searching the record information corresponding to the license plate number; s4, if the record information in the valid period of the pass is not found, the illegal action is not recorded; s5, if the current muck truck is out of the allowed passing time range, the current muck truck is forbidden to pass time illegal behaviors; s6, if the monitoring point position of the current muck truck is not in the road section allowing the passage, determining that the monitoring point position deviates from the route illegal behavior; s7, carrying out statistical analysis on illegal information of the muck car; and S8, the traffic police uniformly inspects and manages the muck vehicles generating the early warning. The method utilizes the prior bayonet and road monitoring system to integrate the pass related information of the muck vehicle record in real time; and can carry out real-time early warning with the dregs car vehicle that the law violation is current, reduced the complexity and the administrative cost of management and control.
Description
Technical Field
The invention relates to a real-time early warning method for a muck truck, and belongs to the technical field of vehicle management.
Background
In recent years, with the development of urban construction, construction sites are seen everywhere, and due to the fact that the construction sites are short of place for managing and controlling the muck trucks, the muck trucks are overloaded at will, and behaviors which do not comply with the restriction regulations sometimes occur. This undoubtedly poses a challenge to the traffic management sector.
At present, several muck truck management systems exist, for example, based on a vehicle-mounted GPS (global positioning system) and monitoring of building site entrances and exits, the running tracks of muck trucks are gathered together, extra equipment needs to be installed, and functions such as early warning and control distribution cannot be realized.
In addition, the currently adopted muck truck monitoring system with the checking function also needs additional specific equipment, can not carry out unified supervision on the muck trucks and is relatively troublesome to operate; for example, chinese patent document CN109523777A discloses a vehicle remote inspection method and system based on a big data monitoring center, the method includes: the inspection terminal sends an inspection instruction to the big data monitoring center; the big data monitoring center determines an inspection area of the inspection terminal according to the position and the inspection range of the inspection terminal in the inspection instruction, and sends a display instruction containing the pre-display information to a muck vehicle management system in the inspection area so that an indicating device outside the muck vehicle in the inspection area displays the pre-display information; the muck truck management system is arranged on a muck truck and comprises an indicating device which is arranged outside the muck truck and is used for displaying pre-display information; however, in this patent, the muck car management system needs to be installed on the muck car, which increases the investment and management costs of the equipment.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a real-time early warning method of a muck car, which utilizes the existing bayonet and a road monitoring system to detect the muck car in real time, identify the license plate number and input the license plate number into a database and integrate the pass related information recorded by the muck car in real time; more importantly, the illegal through muck vehicle can be pre-warned in real time, and the management and control complexity and the management cost are reduced. Meanwhile, the system can also distribute and control specific slag cars and count the historical number of the slag cars.
Interpretation of terms:
a cafe-ssd detection network: a ssd (single Shot multi box detector) target detection network based on the Caffe deep learning framework.
CNN + LSTM + CTC character recognition network: the mainstream OCR character recognition algorithm adopts CNN, LSTM and CTC, the CNN is a convolution neural network, the LSTM is a long-time memory network, and the CTC is a time sequence class classification based on the neural network.
The technical scheme of the invention is as follows:
a real-time early warning method for a muck truck comprises the following steps:
s1, acquiring video pictures of the bayonet, the electric police and the monitoring point in real time;
s2, detecting the muck car in the picture by using a deep learning algorithm and identifying the car type and the license plate of the muck car;
s3, searching the record information corresponding to the license plate number identified in the step S2 from the record database;
s4, if the record information in the valid period of the passage is not found in the record database, judging that illegal behaviors are not recorded, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into the illegal database, and giving out early warning of the illegal behaviors;
if the filing information in the validity period is found in the filing database, performing step S5;
s5, if the current time of the muck vehicle is out of the permitted time range of the record information, determining the illegal act of the forbidden time, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into a illegal database, and giving a driving early warning of the forbidden time;
if the current occurrence time of the slag car is within the time range allowing passage in the record information, the step S6 is carried out;
s6, if the monitoring point position of the current muck vehicle is not in the road section allowed to pass in the recorded information, judging that the muck vehicle deviates from the route illegal action, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into an illegal database and making an early warning of the driving of the muck vehicle deviating from the route;
if the monitoring point position of the current muck truck is in a road section which is allowed to pass in the record information, judging that the monitoring point position is legal;
s7, carrying out statistical analysis on illegal information of the muck vehicle in the illegal database;
and S8, uniformly examining and managing the waste soil vehicles generating early warning by the traffic police according to the statistical analysis result, and punishing the illegal waste soil vehicles according to the evidence obtaining information of the early warning system and the record information of the illegal vehicles, wherein the evidence obtaining information is the video picture obtained in the step S1.
Preferably, in step S2, the deep learning algorithm includes:
the caffe-ssd detection network is used for detecting the muck truck in the picture;
a caffe-mobilene classification network for identifying the type of the muck truck;
and the CNN + LSTM + CTC character recognition network is used for recognizing the license plate of the muck truck.
Caffe-ssd detection network: the industrial grade cafe deep learning network framework, ssd300 processing speed 59fps, meets the real-time requirement. Caffe-mobilenet classification network: the model can be used for the mobile terminal, and the separable convolution is adopted in the core of the model, so that not only can the calculation complexity of the model be reduced, but also the size of the model can be greatly reduced. CNN + LSTM + CTC can realize the recognition of the characters of the indefinite number plate, and the number plate of the muck car is often shielded by the characters, so that the length of the number plate is not fixed.
Preferably, in step S3, the record information includes a license plate of the slag car, a model of the slag car, a name of the car owner, a phone number of the car owner, an address of the car owner, a passing validity period, a passing permission time, and a passing permission road section.
Preferably, in step S4, step S5 and step S6, the muck car passing information includes a checkpoint name of a current muck car, a passing time, a photographed picture, position information of the muck car in the picture, a license plate number and car type information.
According to the present invention, in step S7, the content of the statistical analysis includes the total number of the early warning counts, the working point early warning vehicles, the scheduled vehicles, the processed vehicles, the checked vehicles, the scheduled vehicles, the check time period, the yesterday early warning data statistics, the last week early warning data statistics, the last month early warning data statistics, the non-documented early warning, the no-go time driving early warning, and the deviation route driving early warning, and the scheduled vehicles include the processed vehicles, the checked vehicles, and the scheduled vehicles. The traffic management department can conveniently control the illegal conditions of the muck vehicles in the city/district in a macroscopic manner.
According to the invention, the early warning method further comprises the following steps: and when illegal waste soil vehicles are not found in the field traffic police law enforcement process, distributing and controlling the illegal waste soil vehicles in the early warning system, adding the license plates of the waste soil vehicles needing to be distributed and controlled into the distribution and control early warning system, comparing the license plates with the identification result in the S2 in real time, and immediately giving early warning if the target vehicle is found by the real-time early warning system.
The invention has the beneficial effects that:
1. the invention is based on the existing infrastructure such as road gates and monitoring cameras, and realizes the monitoring and early warning of illegal behaviors of the muck vehicle under the condition of not additionally investing in the infrastructure construction.
2. The invention can provide the illegal information early warning of the muck truck in real time and assist field traffic police to examine the illegal muck truck information; compared with the traditional examination and treatment mode that squat is carried out at the intersection where the muck car often passes through or the muck car is searched by the original vehicle system and then screened, the invention can reduce the manpower input in the examination and treatment process.
3. The invention is based on big data statistical analysis, and is convenient for traffic control departments to have a macro control on the illegal condition of the muck vehicle in the local city/district.
Drawings
Fig. 1 is a flow diagram of a real-time early warning method for a muck truck.
Detailed Description
The invention is further described below, but not limited thereto, with reference to the following examples and the accompanying drawings.
Example 1
A real-time early warning method for a muck truck is shown in figure 1 and comprises the following steps:
s1, acquiring video pictures of the bayonet, the electric police and the monitoring point in real time;
s2, detecting the muck car in the picture by using a deep learning algorithm and identifying the car type and the license plate of the muck car;
in step S2, the deep learning algorithm includes:
the caffe-ssd detection network is used for detecting the muck truck in the picture; the specific process of detecting the muck car in the network detection picture by using the caffe-ssd refers to patent CN 106611162B.
A caffe-mobilene classification network for identifying the type of the muck truck; a lightweight deep neural network in the context of deep learning.
And the CNN + LSTM + CTC character recognition network is used for recognizing the license plate of the muck truck. The concrete process and parameter of CNN + LSTM + CTC character recognition network for recognizing the license plate of the muck truck are set by referring to patent CN109977950A, and the three neural networks are the prior art.
Caffe-ssd detection network: the industrial grade cafe deep learning network framework, ssd300 processing speed 59fps, meets the real-time requirement. Caffe-mobilenet classification network: the model can be used for the mobile terminal, and the separable convolution is adopted in the core of the model, so that not only can the calculation complexity of the model be reduced, but also the size of the model can be greatly reduced. CNN + LSTM + CTC can realize the recognition of the characters of the indefinite number plate, and the number plate of the muck car is often shielded by the characters, so that the length of the number plate is not fixed.
S3, searching the record information corresponding to the license plate number identified in the step S2 from the record database;
in step S3, the filing information includes license plate information, vehicle type information, name of the vehicle owner, phone number of the vehicle owner, address of the vehicle owner, passing validity period, passing permission time, and passing permission section.
S4, if the record information in the valid period of the passage is not found in the record database, judging that illegal behaviors are not recorded, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into the illegal database, and giving out early warning of the illegal behaviors;
if the filing information in the validity period is found in the filing database, performing step S5;
s5, if the current time of the muck vehicle is out of the permitted time range of the record information, determining the illegal act of the forbidden time, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into a illegal database, and giving a driving early warning of the forbidden time;
if the current occurrence time of the slag car is within the time range allowing passage in the record information, the step S6 is carried out;
s6, if the monitoring point position of the current muck vehicle is not in the road section allowed to pass in the recorded information, judging that the muck vehicle deviates from the route illegal action, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into an illegal database and making an early warning of the driving of the muck vehicle deviating from the route;
if the monitoring point position of the current muck truck is in a road section which is allowed to pass in the record information, judging that the monitoring point position is legal;
in steps S4, S5, and S6, the muck car passing information includes the name of the checkpoint where the muck car is currently present, the passing time, the photographed picture, the position information of the muck car in the picture, the license plate number, and the car type information.
S7, carrying out statistical analysis on illegal information of the muck vehicle in the illegal database;
in step S7, the content of statistical analysis includes the total number of the early warnings, the working point location early warning vehicles, the scheduled vehicles, the processed vehicles, the checked vehicles, the scheduled vehicles, the check time period, the yesterday early warning data statistics, the last week early warning data statistics, the last month early warning data statistics, the unreported early warning, the prohibited time driving early warning, and the total number of the deviation route driving early warnings, and the scheduled vehicles include the processed vehicles, the checked vehicles, and the scheduled vehicles. The traffic management department can conveniently control the illegal conditions of the muck vehicles in the city/district in a macroscopic manner.
And S8, uniformly examining and managing the waste soil vehicles generating early warning by the traffic police according to the statistical analysis result, punishing the illegal waste soil vehicles according to the evidence obtaining information of the early warning system and the record information of the illegal vehicles, and obtaining the video pictures of the evidence obtaining information step S1.
The method utilizes the existing bayonet and road monitoring system to detect the muck car in real time, identify the license plate number and enter a database, and integrate the related information of the pass on which the muck car is put on record in real time; more importantly, the illegal through muck vehicle can be pre-warned in real time, and the management and control complexity and the management cost are reduced.
The early warning method further comprises the following steps: and when illegal waste soil vehicles are not found in the field traffic police law enforcement process, distributing and controlling the illegal waste soil vehicles in the early warning system, adding the license plates of the waste soil vehicles needing to be distributed and controlled into the distribution and control early warning system, comparing the license plates with the identification result in the S2 in real time, and immediately giving early warning if the target vehicle is found by the real-time early warning system.
The invention can provide the illegal information early warning of the muck truck in real time and assist field traffic police to examine the illegal muck truck information; compared with the traditional examination and treatment mode that squat is carried out at the intersection where the muck car often passes through or the muck car is searched by the original vehicle system and then screened, the invention can reduce the manpower input in the examination and treatment process.
Claims (6)
1. A real-time early warning method for a muck truck is characterized by comprising the following steps:
s1, acquiring video pictures of the bayonet, the electric police and the monitoring point in real time;
s2, detecting the muck car in the picture by using a deep learning algorithm and identifying the car type and the license plate of the muck car;
s3, searching the record information corresponding to the license plate number identified in the step S2 from the record database;
s4, if the record information in the valid period of the passage is not found in the record database, judging that illegal behaviors are not recorded, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into the illegal database, and giving out early warning of the illegal behaviors;
if the filing information in the validity period is found in the filing database, performing step S5;
s5, if the current time of the muck vehicle is out of the permitted time range of the record information, determining the illegal act of the forbidden time, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into a illegal database, and giving a driving early warning of the forbidden time;
if the current occurrence time of the slag car is within the time range allowing passage in the record information, the step S6 is carried out;
s6, if the monitoring point position of the current muck vehicle is not in the road section allowed to pass in the recorded information, judging that the muck vehicle deviates from the route illegal action, automatically inputting the vehicle passing information and the illegal information of the muck vehicle into an illegal database and making an early warning of the driving of the muck vehicle deviating from the route;
if the monitoring point position of the current muck truck is in a road section which is allowed to pass in the record information, judging that the monitoring point position is legal;
s7, carrying out statistical analysis on illegal information of the muck vehicle in the illegal database;
and S8, uniformly examining and managing the waste soil vehicles generating early warning by the traffic police according to the statistical analysis result, punishing the illegal waste soil vehicles according to the evidence obtaining information of the early warning system and the record information of the illegal vehicles, wherein the evidence obtaining information is the video picture obtained in the step S1.
2. The real-time early warning method for the muck vehicle as claimed in claim 1, wherein in the step S2, the deep learning algorithm comprises:
the caffe-ssd detection network is used for detecting the muck truck in the picture;
a caffe-mobilene classification network for identifying the type of the muck truck;
and the CNN + LSTM + CTC character recognition network is used for recognizing the license plate of the muck truck.
3. The real-time pre-warning method for the muck vehicle as claimed in claim 1, wherein in the step S3, the record information includes license plate of the muck vehicle, model of the muck vehicle, name of the vehicle owner, phone of the vehicle owner, address of the vehicle owner, valid period of passage, allowed passage time and allowed passage section.
4. The real-time early warning method for the muck vehicle as claimed in claim 1, wherein in the steps S4, S5 and S6, the muck vehicle passing information includes a checkpoint point name of the muck vehicle, a vehicle passing time, a photographed picture, position information of the muck vehicle in the picture, a license plate number and vehicle type information.
5. The real-time early warning method for the muck vehicle as claimed in claim 1, wherein in step S7, the content of statistical analysis includes a total number of early warnings, working point early warning vehicles, scheduled vehicles, processed vehicles, inspected vehicles, scheduled vehicles, inspection time periods, yesterday early warning data statistics, last week early warning data statistics, last month early warning data statistics, non-documented early warnings, no-go-time driving early warning and total number of deviation-route driving early warnings, and the scheduled vehicles include processed vehicles, inspected vehicles and scheduled vehicles.
6. The real-time early warning method for the muck vehicle as claimed in claims 1 to 5, further comprising: and when illegal waste soil vehicles are not found in the field traffic police law enforcement process, distributing and controlling the illegal waste soil vehicles in the early warning system, adding the license plates of the waste soil vehicles needing to be distributed and controlled into the distribution and control early warning system, comparing the license plates with the identification result in the S2 in real time, and immediately giving early warning if the target vehicle is found by the real-time early warning system.
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Cited By (1)
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Application publication date: 20211217 |