CN208479837U - A kind of real-time target detection device based on raspberry pie - Google Patents
A kind of real-time target detection device based on raspberry pie Download PDFInfo
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- CN208479837U CN208479837U CN201821181582.8U CN201821181582U CN208479837U CN 208479837 U CN208479837 U CN 208479837U CN 201821181582 U CN201821181582 U CN 201821181582U CN 208479837 U CN208479837 U CN 208479837U
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Abstract
The utility model belongs to target detection technique field, discloses a kind of real-time target detection device based on raspberry pie, and the real-time target detection device based on raspberry pie is provided with raspberry pie development board；Raspberry pie development board is connected with power supply by 3.3v or 5v interface, raspberry pie development board is connected with camera by CSI connector, the HDMI interface of raspberry pie development board is connected with display by HDMI connection VGA patchcord, and the USB interface of raspberry pie development board is connected with keyboard and mouse.The utility model acquires video data by camera, these data can be uploaded to cloud by network by raspberry pie development board, and user can be checked by intelligent terminal.The raspberry pie development board that the utility model is used has the advantage that small in size for other processing boards, structure is simple, suitable for the development and utilization of video object detection, can effectively real-time perfoming target detection, reach better video monitoring effect.
Description
Technical field
The utility model belongs to target detection technique field more particularly to a kind of real-time target detection dress based on raspberry pie
It sets.
Background technique
Currently, the prior art commonly used in the trade is this bar:
Raspberry pie development board is a kind of minicomputer that research and development are supported by Britain's raspberry pie development board foundation, and size is only
Have bank card size, it is cheap, enter in terms of study or scientific research gate threshold tool it is not high, have good compatibility.Tree
The certain kind of berries sends development board not only to come with simple computer operating system, also has very strong Video coding and decoding capability；Therefore it sets
The certain kind of berries send development board exploitation can be used in multiple fields, such as: text-to-speech service, FM station transmitter, Hadoop cluster,
Video object detection and tracking etc..
With the quickening of Development of Urbanization, many cells can all arrange monitoring device, but the cost that monitoring device is high
And the key that monitoring device can not be popularized.
Video object detection technique is mainly by time in computer vision methods subduction video and redundancy spatially
Information has certain sensitivity to target position variation.Current this video object detection technique mainly applies to intelligent prison
In control or target identification and tracking.But the hsrdware requirements for meeting above-mentioned technical proposal in industry are too high, especially X86 chip
It is all expensive with GPU, cause cost too high；Limited transmission distance system；Software opening is poor；Phenomena such as power consumption is higher.
In conclusion problem of the existing technology is:
Existing video object detection device is too high to hardware requirement, causes cost too high；Limited transmission distance system；It is soft
Part opening is poor；Phenomena such as power consumption is higher.
Utility model content
In view of the problems of the existing technology, the utility model provides a kind of real-time target detection dress based on raspberry pie
It sets.
The utility model is that this bar is realized, a kind of real-time target detection device based on raspberry pie is provided with
Raspberry pie development board；
Raspberry pie development board is connected with power supply by 3.3v or 5v interface, and raspberry pie development board is connected by CSI connector
There is camera, the HDMI interface of raspberry pie development board is connected with display, raspberry pie development board by HDMI connection VGA patchcord
USB interface be connected with keyboard and mouse；
Raspberry pie development board includes video data acquiring device, video data processor, moving object detection device and video
Data storage；
Camera mode in raspberry pie development board is configured to video input mode first by video data acquiring device, then
Video data is acquired using V4L2 technology；
Video data processor is after the information for receiving camera acquisition, at 64 ARMV8 of raspberry pie development board
Coded treatment quickly is carried out to video information after managing module, by video data encoding collected at h.264 format；
Moving object detection device builds the convolution mind an of lightweight using MXNet inside raspberry pie exploitation electrode systems
Computer vision system is created through network, and on raspberry pie development board；
In video data memory, mainly by being connected on AWS Cloud, is managed using cloud and carry out lightweight convolution
The management of neural network and the storage of data.
Further, raspberry pie development board is Raspberry Pi3 Model B, is four core Broadcom of a new generation
64 ARMv8 processors of BCM2837, processor speed can reach 1.2GHz, and ARMv8 includes the memory of 1GB RAM；4
USB interface, 40 pin GPIO plugs of expansion, onboard BCM43143WiFi chip support bluetooth and WIFI；HDMI output end
Mouthful；Storage device: microSD；SD card card reader.
Further, camera using Camera V2.1 model camera.
Further, camera is connected by CSI connector.
Further, keyboard is connect with mouse by USB interface.
Further, cable interface or onboard WiFi chip are connected to the network.
Further, electric source line interface connects Android charging cable, voltage 3.3v or 5v.
Further, HDMI output port connection display institute.
Further, the convolutional neural networks model of the raspberry pie Development plank system that microSD storage has been downloaded and training.
The utility model acquires video data by camera, and detecting the rate that it identifies target is 2.067s/ frame, average
Precision reaches 71.24mAP, and storage of the whole system from video data acquiring to training result can reach real-time well
These data are finally uploaded to cloud by network using raspberry pie development board, user can pass through intelligent end by the purpose of property
End is checked.Whole system is all significantly less than monitoring system on the market in cost and operation cost, and this is practical new
The raspberry pie development board that type is used has the advantage that small in size for other processing boards, and structure is simple, is applicable in
In the development and utilization of video object detection, can effectively real-time perfoming target detection, reach better video monitoring effect.
Detailed description of the invention
Fig. 1 is the real-time target structure of the detecting device schematic diagram provided by the embodiment of the utility model based on raspberry pie；
Fig. 2 is the real-time target detection device working principle diagram provided by the embodiment of the utility model based on raspberry pie；
Fig. 3 is the hardware structure diagram of raspberry pie development board provided by the embodiment of the utility model；
Fig. 4 is V4L2 video capture technology schematic diagram provided by the embodiment of the utility model；
In figure: 1, raspberry pie development board；2, power supply；3, camera；4, display；5, keyboard；6, mouse.
Specific embodiment
For the invention, features and effects that can further appreciate that the utility model, the following examples are hereby given, and cooperates
Detailed description are as follows for attached drawing.
As depicted in figs. 1 and 2, the real-time target detection device packet provided by the embodiment of the utility model based on raspberry pie
It includes: raspberry pie development board 1, power supply 2, camera 3, display 4, keyboard 5, mouse 6.
Raspberry pie development board 1 is connected with power supply 2 by 3.3v or 5v interface, and raspberry pie development board 1 is connected by CSI connector
It is connected to camera 3, the HDMI interface of raspberry pie development board 1 is connected with display 4, raspberry pie by HDMI connection VGA patchcord
The USB interface of development board 1 is connected with keyboard 5 and mouse 6；
Raspberry pie development board 1 includes video data acquiring device, video data processor, moving object detection device and video
Data storage；
Video data acquiring device is that the camera mode in raspberry pie development board 1 is configured to video input mode；
Video data processor is after the information for receiving the acquisition of camera 3, at 64 ARMV8 of raspberry pie development board
Quickly video information is handled after reason module；
Moving object detection device builds the convolution of a lightweight using MXNet in 1 internal system of raspberry pie development board
Neural network, and computer vision system is created on raspberry pie development board 1；
Video data memory is managed using cloud mainly by being connected on AWS Cloud and carries out lightweight convolution mind
The storage of management and data through network.
System Working Principle: video data acquiring device master, which plays, connects the completion of camera 3 by CSI connector, first will tree
The certain kind of berries sends the camera mode of the camera 3 of development board 1 to be configured to video input mode.After the completion of configuration, regarded by Linux V4L2
Frequency acquisition technique is acquired video data；Video data processor is after receiving collected video data, by video
Data are encoded, and coded format is h.264 format；Moving object detection device builds one by MXNet deep learning library
A lightweight volume convolutional neural networks, the video data encoded is put into convolutional neural networks and is trained, is obtained
Training result；Video data memory, will be trained as a result, by being connected on AWS Cloud, utilize cloud management to carry out
The management of lightweight convolutional neural networks and the storage of data.
Further, raspberry pie development board 1 is 3 Model B of Raspberry Pi, is four core Broadcom of a new generation
64 ARMv8 processors of BCM2837, processor speed can reach 1.2GHz, and ARMv8 includes the memory of 1GB RAM；4
USB interface, 40 pin GPIO plugs of expansion, onboard BCM43143 WiFi chip support bluetooth and WIFI；HDMI output end
Mouthful；Storage device: microSD；SD card card reader.
Further, camera 3 using Camera V2.1 model camera.
When the utility model is used, the camera 3 transferred is fixed on somewhere, whenever someone passes by its camera 3
When institute's coverage area, video data at this time can be passed through by the internal system that camera 3 is passed to raspberry pie development board 1
ARMV8 processor handles video data.The video data of incoming 1 internal system of raspberry pie development board can be supported a variety of
Storage format be connected to above display 4 including H264, mp4 etc. by HDMI, controlled at the end PC.It is calculated using SSD
Method is realized in 1 inner utilization MXNet deep learning library of raspberry pie development board, builds the convolutional Neural net an of lightweight
Network, the video data being passed to camera 3 are trained, and carry out detection judgement to the target of be passed to video data,
In the region covered, if someone occurs, detection and tracking can be carried out to it, and data at this time are stored, and passes through
It allows and sends out standby and create secure connection in AWS Cloud；AWS loT can be used after connection to create on raspberry pie development board 1
Service, it can near-real-time carries out target detection and result is pushed in AWS Cloud.It can also send out and set at this time
It detects and target detection video data at this time is sent to user by way of mail or short message while target.
The above is only the preferred embodiment to the utility model, is not made in any form to the utility model
Limitation, it is all according to the technical essence of the utility model any simple modification made to the above embodiment, equivalent variations with
Modification, is all within the scope of the technical scheme of the utility model.
Claims (3)
1. a kind of real-time target detection device based on raspberry pie, which is characterized in that the real-time target inspection based on raspberry pie
Device is surveyed to be provided with
Raspberry pie development board；
Raspberry pie development board is connected with power supply by 3.3v or 5v interface, and raspberry pie development board is connected with by CSI connector and is taken the photograph
As head, the HDMI interface of raspberry pie development board is connected with display by VGA patchcord, and the USB interface of raspberry pie development board connects
It is connected to keyboard and mouse；
Raspberry pie development board includes video data acquiring device, video data processor, moving object detection device and video data
Memory；
Video data acquiring device is that the camera mode in raspberry pie development board is configured to video input mode；
Video data processor handles mould after the information for receiving camera acquisition, through 64 ARMV8 of raspberry pie development board
Quickly video information is handled after block；
Moving object detection device builds the convolutional neural networks of a lightweight inside raspberry pie Development plank system, and is setting
The certain kind of berries, which is sent, creates computer vision system on development board；
In video data memory, by being connected on AWS Cloud, is managed using cloud and carry out lightweight convolutional neural networks
Management and data storage.
2. the real-time target detection device based on raspberry pie as described in claim 1, which is characterized in that raspberry pie development board is adopted
With BCM283764 ARMv8 processors of four core Broadcom, ARMv8 includes the memory of 1GB RAM；4 USB interfaces expand
The 40 pin GPIO plugs filled, onboard BCM43143WiFi chip support bluetooth and WIFI；HDMI output port；Storage device:
microSD；SD card card reader.
3. the real-time target detection device based on raspberry pie as described in claim 1, which is characterized in that camera using
The camera of Camera V2.1 model.
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| CN201821181582.8U CN208479837U (en) | 2018-07-25 | 2018-07-25 | A kind of real-time target detection device based on raspberry pie |
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| CN201821181582.8U CN208479837U (en) | 2018-07-25 | 2018-07-25 | A kind of real-time target detection device based on raspberry pie |
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Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109981378A (en) * | 2019-04-16 | 2019-07-05 | 陈麒任 | A kind of network detection and analysis tool and its application method based on raspberry pie |
| CN110110639A (en) * | 2019-04-29 | 2019-08-09 | 济南浪潮高新科技投资发展有限公司 | A kind of Indoor Video method and Indoor Video vehicle based on artificial intelligence tracking |
| CN110472541A (en) * | 2019-08-05 | 2019-11-19 | 福州大学 | Garment identification system and method based on raspberry pie |
| CN110569827A (en) * | 2019-09-28 | 2019-12-13 | 华南理工大学 | Face recognition reminding system based on convolutional neural network |
| CN110609225A (en) * | 2019-11-08 | 2019-12-24 | 伟创力电子技术(苏州)有限公司 | PCBA general test platform based on raspberry group |
| CN111251299A (en) * | 2020-02-21 | 2020-06-09 | 广东工业大学 | Hadoop-based cleaning type cloud robot system |
| CN112309068A (en) * | 2020-10-29 | 2021-02-02 | 电子科技大学中山学院 | Forest fire early warning method based on deep learning |
| CN113743242A (en) * | 2021-08-13 | 2021-12-03 | 成都理工大学 | Landslide mass detection and identification method based on raspberry pie |
-
2018
- 2018-07-25 CN CN201821181582.8U patent/CN208479837U/en not_active Expired - Fee Related
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109981378A (en) * | 2019-04-16 | 2019-07-05 | 陈麒任 | A kind of network detection and analysis tool and its application method based on raspberry pie |
| CN110110639A (en) * | 2019-04-29 | 2019-08-09 | 济南浪潮高新科技投资发展有限公司 | A kind of Indoor Video method and Indoor Video vehicle based on artificial intelligence tracking |
| CN110472541A (en) * | 2019-08-05 | 2019-11-19 | 福州大学 | Garment identification system and method based on raspberry pie |
| CN110569827A (en) * | 2019-09-28 | 2019-12-13 | 华南理工大学 | Face recognition reminding system based on convolutional neural network |
| CN110569827B (en) * | 2019-09-28 | 2024-01-05 | 华南理工大学 | Face recognition reminding system based on convolutional neural network |
| CN110609225A (en) * | 2019-11-08 | 2019-12-24 | 伟创力电子技术(苏州)有限公司 | PCBA general test platform based on raspberry group |
| CN111251299A (en) * | 2020-02-21 | 2020-06-09 | 广东工业大学 | Hadoop-based cleaning type cloud robot system |
| CN112309068A (en) * | 2020-10-29 | 2021-02-02 | 电子科技大学中山学院 | Forest fire early warning method based on deep learning |
| CN113743242A (en) * | 2021-08-13 | 2021-12-03 | 成都理工大学 | Landslide mass detection and identification method based on raspberry pie |
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