CN213042437U - Vehicle flow detection system based on deep learning - Google Patents
Vehicle flow detection system based on deep learning Download PDFInfo
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- CN213042437U CN213042437U CN202021551109.1U CN202021551109U CN213042437U CN 213042437 U CN213042437 U CN 213042437U CN 202021551109 U CN202021551109 U CN 202021551109U CN 213042437 U CN213042437 U CN 213042437U
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- 238000001514 detection method Methods 0.000 title claims abstract description 34
- 238000013135 deep learning Methods 0.000 title claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 13
- 238000013500 data storage Methods 0.000 claims abstract description 8
- 230000004044 response Effects 0.000 claims abstract description 6
- 230000006698 induction Effects 0.000 claims description 4
- 238000005259 measurement Methods 0.000 abstract 1
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Abstract
The utility model discloses a traffic flow detection system based on deep learning, including data processor, road detection module, data storage device, communication device, display device, cloud server, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module; road detection module, data storage device, communication device, display device, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module respectively with data processor connect, high in the clouds server with communication device communication connection. Through the utility model discloses, can realize the measurement of higher accuracy.
Description
Technical Field
The utility model belongs to the technical field of the traffic and specifically relates to a traffic flow detecting system based on degree of depth study.
Background
The traffic flow data acquisition is the basis of an intelligent traffic system, and the acquisition system inputs video data of a traffic monitoring camera, identifies vehicles on a road from a picture, counts and outputs time sequence data. In addition, the existing system has low resistance to the illumination change of the road environment, and the accuracy of traffic flow statistics is influenced.
SUMMERY OF THE UTILITY MODEL
The utility model aims to overcome the defects of the prior art and provide a traffic flow detection system based on deep learning, which comprises a data processor, a road detection module, a data storage device, a communication device, a display device, a cloud server, a power module, a velocimeter, a video acquisition device, a lighting device, a noise monitoring module and a mobile induction module; road detection module, data storage device, communication device, display device, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module respectively with data processor connect, high in the clouds server with communication device communication connection.
Preferably, the road detection module adopts an intelligent road event detection device, and the model of the intelligent road event detection device is IR 100S.
Preferably, the velocimeter adopts an MCS-II intelligent velocimeter.
Preferably, the video acquisition device comprises a camera, an image processor, an electric holder, an analog-to-digital converter and a vibration sensor; the vibration sensor is connected with the analog-to-digital converter, the camera is connected with the image processor, and the image processor, the electric holder and the analog-to-digital converter are respectively connected with the data processor; the camera is only arranged on the electric pan-tilt head.
Preferably, the lighting device comprises a light sensor, an analog-to-digital converter II, an LED lamp and an LED lamp controller; the LED lamp is connected with the LED lamp controller, the light sensor is connected with the second analog-to-digital converter, and the second analog-to-digital converter and the LED lamp controller are respectively connected with the data processor.
Preferably, the noise monitoring module comprises a noise sensor, and the noise sensor is connected with the analog-to-digital converter.
Preferably, the mobile induction module adopts a mobile inductor, the mobile inductor adopts a microwave mobile inductor, and the type of the microwave mobile inductor is MI 701.
Preferably, the image processor is of the type OV 07960-E62P.
The utility model has the advantages that: the utility model discloses a road detection module, video acquisition device and lighting device mutually support and realize the traffic flow detection of higher accuracy.
Drawings
Fig. 1 is a schematic diagram of a deep learning-based traffic flow detection system.
Detailed Description
The technical solution of the present invention is described in further detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following description.
As shown in fig. 1, the utility model provides a traffic flow detection system based on deep learning, including data processor, road detection module, data storage device, communication device, display device, cloud server, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module; the road detection module is used for detecting road traffic flow and acquiring traffic flow information; the velocimeter is used for measuring the speed of a single vehicle; the video acquisition device is used for acquiring real-time traffic flow image information; the lighting device is used for supplementing light when the light illumination is lower than the set illumination; the noise monitoring module is used for detecting the noise intensity in real time; the movement sensing module is used for detecting the movement of people.
Road detection module, data storage device, communication device, display device, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module respectively with data processor connect, high in the clouds server with communication device communication connection.
The specific road detection module adopts an intelligent road event detection device, and the model of the intelligent road event detection device is IR 100S. The velocimeter adopts an MCS-II intelligent velocimeter.
The video acquisition device comprises a camera, an image processor, an electric cradle head, an analog-to-digital converter and a vibration sensor; the vibration sensor is connected with the analog-to-digital converter, the camera is connected with the image processor, and the image processor, the electric holder and the analog-to-digital converter are respectively connected with the data processor; the camera is only arranged on the electric pan-tilt head.
The lighting device comprises a light ray sensor, an analog-to-digital converter II, an LED lamp and an LED lamp controller; the LED lamp is connected with the LED lamp controller, the light sensor is connected with the second analog-to-digital converter, and the second analog-to-digital converter and the LED lamp controller are respectively connected with the data processor.
The noise monitoring module comprises a noise sensor which is connected with the analog-to-digital converter.
The mobile induction module adopts a mobile inductor, the mobile inductor adopts a microwave mobile inductor, and the type of the microwave mobile inductor is MI 701.
The image processor is of the type OV 07960-E62P.
The foregoing is illustrative of the preferred embodiments of the present invention, and it is to be understood that the invention is not limited to the precise forms disclosed herein, and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the invention as defined by the appended claims. But that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention, which is to be limited only by the claims appended hereto.
Claims (8)
1. A traffic flow detection system based on deep learning is characterized by comprising a data processor, a road detection module, a data storage device, a communication device, a display device, a cloud server, a power supply module, a velocimeter, a video acquisition device, a lighting device, a noise monitoring module and a mobile induction module; road detection module, data storage device, communication device, display device, power module, tachymeter, video acquisition device, lighting device, noise monitoring module, removal response module respectively with data processor connect, high in the clouds server with communication device communication connection.
2. The deep learning-based traffic flow detection system according to claim 1, wherein the road detection module employs an intelligent road event detection device, and the intelligent road event detection device employs a model of IR 100S.
3. The deep learning-based traffic flow detection system according to claim 1, wherein the velocimeter adopts an MCS-II intelligent velocimeter.
4. The deep learning-based traffic flow detection system according to claim 1, wherein the video acquisition device comprises a camera, an image processor, an electric pan-tilt, an analog-to-digital converter and a vibration sensor; the vibration sensor is connected with the analog-to-digital converter, the camera is connected with the image processor, and the image processor, the electric holder and the analog-to-digital converter are respectively connected with the data processor; the camera is arranged on the electric holder.
5. The deep learning-based traffic flow detection system according to claim 1, wherein the lighting device comprises a light sensor, a second analog-to-digital converter, an LED lamp and an LED lamp controller; the LED lamp is connected with the LED lamp controller, the light sensor is connected with the second analog-to-digital converter, and the second analog-to-digital converter and the LED lamp controller are respectively connected with the data processor.
6. The deep learning based vehicle flow detection system of claim 4, wherein the noise monitoring module comprises a noise sensor, and the noise sensor is connected to the analog-to-digital converter.
7. The deep learning-based traffic flow detection system according to claim 1, wherein the mobile sensor module is a mobile sensor, the mobile sensor is a microwave mobile sensor, and the microwave mobile sensor is of type MI 701.
8. The deep learning based traffic flow detection system according to claim 4, wherein the image processor is of type OV 07960-E62P.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021551109.1U CN213042437U (en) | 2020-07-30 | 2020-07-30 | Vehicle flow detection system based on deep learning |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202021551109.1U CN213042437U (en) | 2020-07-30 | 2020-07-30 | Vehicle flow detection system based on deep learning |
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| CN213042437U true CN213042437U (en) | 2021-04-23 |
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| CN202021551109.1U Expired - Fee Related CN213042437U (en) | 2020-07-30 | 2020-07-30 | Vehicle flow detection system based on deep learning |
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114023083A (en) * | 2021-09-08 | 2022-02-08 | 杭州永明大科技有限公司 | Road driving safety risk prediction early warning emergency rescue device and method |
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2020
- 2020-07-30 CN CN202021551109.1U patent/CN213042437U/en not_active Expired - Fee Related
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114023083A (en) * | 2021-09-08 | 2022-02-08 | 杭州永明大科技有限公司 | Road driving safety risk prediction early warning emergency rescue device and method |
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Granted publication date: 20210423 |