CN112699715A - Storage battery car upstairs intelligent management and control system based on deep learning - Google Patents
Storage battery car upstairs intelligent management and control system based on deep learning Download PDFInfo
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Abstract
The invention aims to provide the battery car upstairs control system which is high in identification accuracy and accurate in identification, so that the battery car can be prevented from upstairs, and normal use of residents is not influenced. The invention relates to an intelligent control system for battery car going upstairs, which comprises: the image acquisition module is arranged in the elevator and used for acquiring real-time images in the elevator so as to obtain images in the elevator; the storage battery car detection module is used for sequentially carrying out target detection on the images in the elevator acquired by the image acquisition module so as to detect whether the storage battery car exists in the images in the elevator; and the judgment control module judges whether the battery car exists in the elevator according to the detection result of the battery car detection module and controls the elevator to keep the door opening state when judging that the battery car exists so as to prevent the battery car from going upstairs through the elevator, wherein the battery car detection module comprises a target detection neural network formed by an improved Tiny-YOLOV3 network Eyevator Net.
Description
Technical Field
The invention relates to an intelligent control system for battery car going upstairs based on deep learning.
Background
The battery car is convenient to use and low in price, so that the battery car is widely used as a vehicle.
With the increase of the use of the battery cars, the characteristic that the battery cars need to be charged all the time also gradually brings about the problem of residential community management. For example, some residents carry battery cars upstairs through elevators for private use and charge the batteries in public areas such as rooms or corridors for their convenience. After the battery car is used for a period of time, the connecting lines in the car are easy to age and short-circuit, and when the short-circuit occurs and the external temperature is high, the short-circuit can easily burn and cause fire, so that tragedy is generated.
In order to prevent accidents, the property departments of a plurality of communities set regulations for forbidding the battery cars to go upstairs; in order to effectively prevent residents from carrying the battery cars upstairs and realize the control of upstairs of the battery cars, certain battery car upstairs control systems appear in the prior art. For example, the battery car is detected by laying a ground induction coil, or the battery car is detected by a traditional image recognition method (such as artificial design features), and the battery car is prevented from being carried by residents to go upstairs when the battery car is detected. These methods have several disadvantages, including: the detection is inaccurate, so that the battery car is released, and the effect of prevention cannot be achieved; the error identification rate is high, people, bicycles, baby carriages and the like are identified as the battery cars by mistake, the elevator is prevented from being loaded, and the normal use of residents is influenced; and so on.
Disclosure of Invention
In order to solve the problems, the invention provides the battery car upstairs control system which has high identification accuracy and accurate identification, so that the battery car upstairs can be prevented, and the normal use of residents is not influenced, and the invention adopts the following technical scheme:
the invention provides an intelligent battery car upstairs management and control system based on deep learning, which is characterized by comprising the following components: the image acquisition module is arranged in the elevator and used for acquiring real-time images in the elevator so as to obtain images in the elevator; the storage battery car detection module is used for sequentially carrying out target detection on the images in the elevator acquired by the image acquisition module so as to detect whether the storage battery car exists in the images in the elevator; and the judgment control module judges whether the battery car exists in the elevator according to the detection result of the battery car detection module and controls the elevator to keep the door opening state when judging that the battery car exists so as to prevent the battery car from going upstairs through the elevator, wherein the battery car detection module comprises a target detection neural network formed by an improved Tiny-YOLOV3 network Eyevator Net.
The battery car upstairs intelligent management and control system based on deep learning provided by the invention can also comprise: the voice module is arranged in the elevator, and when the storage battery car is judged to exist in the elevator, the judgment control module controls the elevator to keep the door opening state and controls the voice module to play voice prompt in the elevator.
The battery car upstairs intelligent management and control system based on deep learning provided by the invention can also comprise: the state monitoring module is used for detecting the working state of each module and forming corresponding working state information; and the communication module is used for sending the working state information to the outside.
In the intelligent control system for battery car going upstairs based on deep learning, provided by the invention, the image acquisition module can also comprise a wide-angle digital camera arranged in the elevator.
In the battery car upstairs intelligent control system based on deep learning, parts except the wide-angle digital camera in the battery car upstairs intelligent control system can be integrated in the control box to form a corresponding control terminal.
The intelligent control system for the battery car going upstairs based on deep learning provided by the invention can also comprise: and the power supply module is used for supplying power to each module in the intelligent control system for the battery car going upstairs.
The intelligent control system for the battery car going upstairs based on deep learning provided by the invention can also comprise: the remote control module is connected with the switch unit and used for being matched with the switch unit to enable a manager to start or close the intelligent control system of the battery car going upstairs through the switch unit.
Action and Effect of the invention
According to the intelligent control system for the battery car going upstairs based on deep learning, which is provided by the invention, the target detection neural network formed by the improved Tiny-YOLOV3 network EyevattorNet is adopted to detect the target of the image in the elevator, so that the intelligent control system has stronger capability of extracting image information and higher identification accuracy, and is not easy to cause misjudgment. The equipment is tested on site, the system runs stably, and the control effect is excellent. The target detection is based on the AI image recognition technology, and compared with the traditional detection methods such as mode recognition and embedding of the ground induction coil, the detection method is more reliable and stable, and can effectively solve the potential safety hazard caused by the charging problem of the battery car going upstairs.
Drawings
FIG. 1 is a block diagram of an intelligent control system for going upstairs of a battery car based on deep learning according to an embodiment of the invention;
FIG. 2 is a schematic structural diagram of a Tiny-YOLOV3 network EyevattorNet according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the detection result of the Tiny-YOLOV3 network EyevattorNet according to the embodiment of the present invention;
fig. 4 is a flowchart of the operation of the management and control system according to the embodiment of the present invention.
Detailed Description
Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings and examples.
< example >
Fig. 1 is a block diagram of an intelligent control system for going upstairs of a battery car based on deep learning in an embodiment of the invention.
As shown in fig. 1, an intelligent battery car upstairs management and control system (hereinafter referred to as a management and control system) 100 based on deep learning of the embodiment can be divided into a core part and a peripheral part, and specifically includes an image acquisition module 10, a battery car detection module 20, a judgment control module 30, a voice module 40, a state monitoring module 50, a communication module 60, a power supply module 70, and a remote control module 80.
The image acquisition module 10 is disposed in the elevator, and in this embodiment the image acquisition module 10 includes a wide-angle digital camera capable of acquiring real-time images of the interior of the elevator car (i.e., in-elevator images). The acquired real-time image data is divided into two paths, one path of data is transmitted to a community monitoring room (for example, transmitted to a security monitoring display of the community monitoring room for displaying), and the other path of data is used as the input of the storage battery car detection module 20. That is, the camera in the image acquisition module 10 of the present embodiment is used as a security monitoring camera at the same time. In other embodiments, the camera can also be obtained by modifying the circuit of the camera in the original elevator in the community monitoring system, and the real-time image data is divided into one path to be input into the storage battery car detection module 20.
The storage battery car detection module 20 is used for performing target detection on the images in the elevator acquired by the image acquisition module 10, so as to detect whether the storage battery car exists in the images in the elevator. In this embodiment, the camera acquires real-time images in the elevator in a video form, a video stream of the camera includes a plurality of real-time image frames in a time sequence, and the storage battery car detection module 20 sequentially acquires the real-time image frames in the time sequence and sequentially performs target detection on the real-time image frames.
In this embodiment, the battery car detection module 20 includes a target detection neural network, which is specifically a modified Tiny-YOLOV3 network eyevattornet.
FIG. 2 is a schematic structural diagram of a Tiny-YOLOV3 network EyevattorNet according to an embodiment of the present invention.
As shown in fig. 2, the Tiny-YOLOV3 network eyevatorcet adopted in this embodiment is obtained by improving an existing Tiny-YOLOV3 neural network model, and specifically comprises the following layer structures: 416 × 3, multiple convolution layers (Conv/Bn/Lk × n, n ═ 1 or 7), multiple maximum pooling layers (maxpoling), feature layers X1 of 26 × 256, feature layers X2 of 13 × 256, feature layers X2 of UpSampling layers UpSampling2D, 26 × 128, fusion layers Concatenate [ X1, X2], output Y1, output Y2.
The EyevattorNet is obtained by adopting an image training set related to the storage battery car to train after being constructed, and the real-time image frames are uniformly converted into gray images to be input, so that the detection result of whether the storage battery car is contained in the real-time images can be obtained; in addition, the hardware form of the EyevatorNet is realized by Jetson-nano.
FIG. 3 is a schematic diagram of the detection result of the Tiny-YOLOV3 network EyevattorNet according to the embodiment of the present invention.
As shown in fig. 3, the Tiny-YOLOV3 network eyevattornet can detect the target of the real-time image in the elevator and detect the battery car therein (in-frame part). In addition, the template frame for target detection in this embodiment adopts a dimensional clustering method of YOLOV3 to perform dimensional clustering on all targets, and additionally performs dimensional clustering on each detected category, and averages two groups of anchors obtained as the final template frame, and the template frame obtained by field testing through the above strategies is significantly improved in recognition accuracy. Moreover, the target detection network of the embodiment adds an image enhancement strategy (illumination, angle, stretching transformation, etc.) to a training mechanism, so that the model has stronger robustness, and the dependence on the environment can be effectively reduced under the same identification precision.
In this embodiment, the process of determining that the storage battery car exists in the current elevator by the determination control module 30 is as follows: when the storage battery car detection module 20 detects that one real-time image frame contains the storage battery car, whether more than 3 real-time image frames with the storage battery car are detected again in the next second or not is further judged, and if more than three real-time image frames are detected again, the storage battery car is judged to exist in the elevator at present.
In addition, the hardware form of the judgment control module 30 of this embodiment is implemented by using a main control board, and the control of the elevator door opening can be implemented by accessing the control system of the elevator and sending an elevator door opening signal to the control system.
In this embodiment, the voice module 40 includes a speaker electrically connected to the main control board. After the judgment control module 30 judges that the battery car exists in the current elevator, the voice module 40 is controlled to play a voice alarm to prompt residents carrying the battery car while controlling the elevator door not to be closed, for example, playing 'to guarantee fire safety and forbid the battery car from going upstairs' and the like.
The state monitoring module 50 is used for detecting the working state of each module in the management and control system 100 and forming corresponding working state information.
The communication module 60 is a 4G cellular network communication module, and is configured to send out operating state information and the like. For example, to a back-office management system responsible for maintaining the management and control system 100, so as to inform maintenance personnel to timely repair when a fault occurs.
The power module 70 is used for supplying power to each module in the management and control system 100 to ensure the normal operation of the management and control system 100.
The remote control module 80 is connected to a switch unit (not shown) disposed in the community monitoring room, and is configured to enable a community manager to start or shut down the entire management and control system 100 through the switch unit (i.e., enable or disconnect the power module 70, so as to enable the entire management and control system 100 to start or shut down). The remote control module 80 may also be accessed to an indoor monitoring system of a community, so that a community manager controls the start or the stop of the management and control system 100 through a monitoring system terminal PC.
In this embodiment, the storage battery car detection module 20, the judgment control module 30, the voice module 40, the state monitoring module 50, the communication module 60, the power module 70 and the remote control module 80 are all integrated in one control box, so as to form a management and control terminal. This control box casing can remain net gape, USB3.0 interface, HDMI interface etc. to transmit the real-time image of camera to the monitoring room of community through LAN. Meanwhile, since the hardware of the management and control system 100 is integrated except for the camera of the image acquisition module 10, the management and control system is small in size and light in weight (the size of the actually manufactured management and control terminal is 145mm × 85mm × 65mm, and the weight is only 0.82Kg), so that the installation, maintenance or replacement is facilitated. When the monitoring camera exists in the elevator, the camera is connected after the management and control terminal is installed, and the arrangement of the whole management and control system 100 does not need complex operation and is easy to realize. In addition, even there is not originally the camera in the elevator, the management and control terminal of this embodiment also only needs the installation and can carry out work with an external camera.
The following describes the work flow of the management and control system 100 of the present embodiment with reference to the drawings.
Fig. 4 is a flowchart of the operation of the management and control system according to the embodiment of the present invention.
As shown in fig. 4, after the management and control system 100 is started and initialized, the work flow includes the following steps:
step S1, the status monitoring module 50 determines whether the image acquisition module 10 has an image input, if no image input, generates an abnormal status prompt and sends the abnormal status prompt to the communication module 60, and if an image input exists, the process goes to step S2;
step S2, the storage battery car detection module 20 continuously performs storage battery car detection on the real-time image acquired by the image acquisition module 10, and if the storage battery car is detected, the step S3 is performed;
step S3, the judgment control module 30 judges whether more than 3 real-time image frames with battery cars are detected again in the next 1 second, and when the detection is finished again, the judgment control module judges that the battery cars exist in the elevator and then enters step S4;
and step S4, the judgment control module 30 controls the voice module 40 to give a voice alarm and controls the elevator not to close the door.
The above process is repeated in real time until the whole management and control system 100 is turned off by the remote control module 80.
Examples effects and effects
According to the management and control system provided by the embodiment, the target detection neural network formed by the improved Tiny-YOLOV3 network eyevattornet is adopted to detect the target of the image in the elevator, so that the management and control system has stronger capability of extracting image information and higher identification accuracy, and is not easy to make misjudgment. The equipment is tested on site, the system runs stably, and the control effect is excellent. The target detection of this embodiment is based on AI image recognition technology, compares with traditional mode identification and detection methods such as burying underground induction coil, and is more reliable and stable, can effectively solve the potential safety hazard that the storage battery car upstairs problem of charging brought.
In addition, compared with the original Tiny-YOLOV3 network structure, the eyevattornet obtained by the embodiment after improvement has stronger image information extraction capability, and can greatly improve the identification accuracy: an image enhancement strategy is added to a training network strategy, and a good recognition effect can be shown without additional supplementary lighting under the low-light condition; the detection preselection frame is obtained by respectively clustering the dimensions of each type of detection object and averaging the whole clusters, and has better identification effect compared with the preselection frame obtained only aiming at the whole clusters. The field test also shows that the EyevatorNet of the embodiment has better identification effect.
In the embodiment, when the storage battery car detection module detects that one real-time image frame contains the storage battery car, the judgment control module further judges whether more than 3 real-time image frames with the storage battery car are detected within the next second or not, if more than three real-time image frames with the storage battery car are detected, the storage battery car is judged to exist in the current elevator, and the judgment mode can further effectively reduce misjudgment.
In this embodiment, except that the camera other constitutional parts all integrate in the control box, therefore small, light in weight, integrated level height, design compactness, simple to operate is swift.
In this embodiment, the real-time image data that the camera obtained is except as storage battery car detection module's input, still divides out the input as the community monitor all the way, therefore the management and control system of this embodiment still has a tractor serves several purposes' effect.
The above embodiments are only used to illustrate different embodiments of the present invention, and the protection scope of the present invention is not limited to the description scope of the above embodiments.
Claims (7)
1. The utility model provides a storage battery car intelligence management and control system upstairs based on degree of depth study which characterized in that includes:
the image acquisition module is arranged in the elevator and used for acquiring real-time images in the elevator so as to obtain images in the elevator;
the storage battery car detection module is used for sequentially carrying out target detection on the images in the elevator acquired by the image acquisition module so as to detect whether the storage battery car exists in the images in the elevator; and
the judging control module judges whether the battery car exists in the elevator according to the detection result of the battery car detection module and controls the elevator to keep the door opening state when judging that the battery car exists so as to prevent the battery car from going upstairs through the elevator,
the storage battery car detection module comprises a target detection neural network formed by a modified Tiny-YOLOV3 network EyevattorNet.
2. The deep learning-based battery car upstairs intelligent management and control system according to claim 1, further comprising:
a voice module disposed in the elevator,
when the storage battery car is judged to exist in the elevator, the judgment control module controls the elevator to keep the door opening state and simultaneously controls the voice module to play voice prompt into the elevator.
3. The deep learning-based battery car upstairs intelligent management and control system according to claim 1, further comprising:
the state monitoring module is used for detecting the working state of each module and forming corresponding working state information; and
and the communication module is used for sending the working state information outwards.
4. The deep learning-based battery car upstairs intelligent management and control system according to claim 1, characterized in that:
wherein, the image acquisition module comprises a wide-angle digital camera arranged in the elevator.
5. The deep learning-based battery car upstairs intelligent management and control system according to claim 4, wherein:
the battery car upstairs intelligent management and control system is characterized in that the battery car upstairs intelligent management and control system is provided with a wide-angle digital camera, and the wide-angle digital camera is arranged on the battery car upstairs intelligent management and control system.
6. The deep learning-based battery car upstairs intelligent management and control system according to claim 1, further comprising:
and the power supply module is used for supplying power to each module in the intelligent control system for the battery car going upstairs.
7. The deep learning-based battery car upstairs intelligent management and control system according to claim 6, further comprising:
the remote control module is connected with the switch unit and used for being matched with the switch unit so as to enable a manager to start or close the intelligent control system of the battery car going upstairs through the switch unit.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN113702833A (en) * | 2021-06-30 | 2021-11-26 | 中国电信集团工会上海市委员会 | Corridor battery car monitoring system and method |
| CN115108412A (en) * | 2022-07-27 | 2022-09-27 | 浙江万立宏科技有限公司 | Elevator voice linkage control system |
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| CN110002315A (en) * | 2018-11-30 | 2019-07-12 | 浙江新再灵科技股份有限公司 | Vertical ladder electric vehicle detection method and warning system based on deep learning |
| CN110210452A (en) * | 2019-06-14 | 2019-09-06 | 东北大学 | It is a kind of based on improve tiny-yolov3 mine truck environment under object detection method |
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| CN110002315A (en) * | 2018-11-30 | 2019-07-12 | 浙江新再灵科技股份有限公司 | Vertical ladder electric vehicle detection method and warning system based on deep learning |
| CN110210452A (en) * | 2019-06-14 | 2019-09-06 | 东北大学 | It is a kind of based on improve tiny-yolov3 mine truck environment under object detection method |
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| CN113702833A (en) * | 2021-06-30 | 2021-11-26 | 中国电信集团工会上海市委员会 | Corridor battery car monitoring system and method |
| CN113702833B (en) * | 2021-06-30 | 2024-04-19 | 中国电信集团工会上海市委员会 | Corridor storage battery car monitoring system and method |
| CN115108412A (en) * | 2022-07-27 | 2022-09-27 | 浙江万立宏科技有限公司 | Elevator voice linkage control system |
| CN115108412B (en) * | 2022-07-27 | 2023-08-22 | 浙江万立宏科技有限公司 | Elevator voice linkage control system |
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