CN113343891A - Detection device and detection method for child kicking quilt - Google Patents
Detection device and detection method for child kicking quilt Download PDFInfo
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- CN113343891A CN113343891A CN202110701250.8A CN202110701250A CN113343891A CN 113343891 A CN113343891 A CN 113343891A CN 202110701250 A CN202110701250 A CN 202110701250A CN 113343891 A CN113343891 A CN 113343891A
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- 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
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- 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
- G08B21/18—Status alarms
- G08B21/24—Reminder alarms, e.g. anti-loss alarms
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/10—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using wireless transmission systems
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B6/00—Tactile signalling systems, e.g. personal calling systems
Abstract
The invention discloses a device and a method for detecting kicking of a child. The detection device comprises an image acquisition module and an image analysis module, and the detection method comprises the following steps: the pajamas worn by children comprise preset patterns; an image acquisition module of the detection device acquires a video or an image of a position where a child sleeps; an image analysis module of the detection device receives the video or the image of the image acquisition module and identifies whether a preset pattern exists in the video or the image; if the preset pattern is detected, it is determined that the child's quilt is not covered. The detection method has low requirement on the surrounding environment, can be used for identifying in a night vision environment, does not need to worry about color conflict, and has high detection accuracy.
Description
Technical field ]
The invention relates to child sleep monitoring, in particular to a device and a method for detecting kicking of a child.
Background art ]
The cold of children is mostly caused by kicking off the quilt covered on the body at night, especially for children under 10 years old. Parents always worry about that children kick off quilts, lift heart and hang gallbladder to sleep without being substantial, which affects the body of the parents and the next day of work.
The invention with the application number of 202010050263.9 discloses a child quilt kicking monitoring and reminding system, which comprises a camera, a first wireless communication module, a first processor, a vibrating bracelet and a pajamas, wherein the pajamas are worn on the body of a child and provided with identification marks during sleeping, the vibrating bracelet is worn on the wrist of a parent, the first wireless communication module is connected with the first processor, and the first processor is in communication connection with the vibrating bracelet through the first wireless communication module; the utility model discloses a children sleep state, including the camera, the camera is connected with first treater, and the camera is used for shooing child's sleeping state, and first treater is arranged in discernment camera shooting scene whether have the colour of discernment sign on the children pajama in the pattern to send vibration signal to the vibration bracelet when discerning the colour of discernment sign on the pajama, the colour of discernment sign is different from other colours except the discernment sign in the camera shooting scene pattern.
The invention determines whether the quilt is covered well or not by identifying the designated color, has high requirement on the environment, and can give a false alarm if the designated color or the color close to the designated color exists in the visual field range of the camera. The use is influenced by ambient light, is not suitable for night moreover, uses infrared lamp night usually, and the image that its camera sensed is the grey map, is difficult to discern the colour.
Summary of the invention
The invention aims to provide a detection device for children kicking quilt, which has low requirements on environment and high detection accuracy.
The invention also aims to solve the technical problem of providing a child kicking quilt detection method which has low requirements on environment and high detection accuracy.
In order to solve the technical problem, the invention adopts the technical scheme that the child quilt kicking detection device comprises an image acquisition module and an image analysis module, wherein the image analysis module receives a video or an image of the image acquisition module, identifies whether a preset pattern exists in the video or the image, and judges that a child quilt is not covered well if the preset pattern is detected.
The detection device for the child kicking quilt comprises a wireless communication module, and when the image analysis module detects the preset pattern, the information of the child kicking quilt is sent out through the wireless communication module.
In the detection device for child kicking quilt, the image analysis module is deployed in the edge calculation box, and the image analysis module uses the edge calculation box to perform operation.
The device for detecting the kicking of the child comprises a pajama and/or an abdominal belt worn by the child, wherein the pajama and/or the abdominal belt comprises at least one preset pattern.
According to the detection device for the child kicking quilt, the preset patterns are arranged with the pajamas and/or the abdominal belts in a split mode, and the patterns are fixedly connected with the pajamas and/or the abdominal belts through inlaying or sewing or adhering or bayonets.
A method for detecting a child quilt kicked by a child comprises the device for detecting the child quilt kicked by the child, and the detection process comprises the following steps:
601) the pajamas and/or the bellyband worn by the children comprise at least one preset pattern;
602) an image acquisition module of the detection device acquires a video or an image of a position where a child sleeps;
603) an image analysis module of the detection device receives the video or the image of the image acquisition module and identifies whether a preset pattern exists in the video or the image;
604) if the image analysis module detects the preset pattern, the child is judged not to be well covered.
In step 604, if the image analysis module detects a preset pattern, the image analysis module sends information of the child kicking the quilt to a parent's mobile phone and/or a bracelet through the wireless communication module, and the mobile phone and/or the bracelet gives an alarm after receiving the information of the child kicking the quilt.
In the method for detecting a child kicked quilt described above, in step 601, the pajamas and/or the abdominal belts worn by the child include a plurality of preset patterns; the recognition algorithm of the image analysis module employs a target detection neural network. Training a target detection neural network to enable the target detection neural network to recognize various preset patterns; in step 604, if the image analysis module detects one or more of the preset patterns, the image analysis module sends information about the child kicking the quilt to the parent's mobile phone and/or bracelet through the wireless communication module.
According to the detection method for child kicking quilt, the image acquisition module is a network camera, the image analysis module is arranged on the cloud server, and the camera transmits the captured video or image to the cloud server; and the image analysis module of the cloud server sends the information of kicking the quilt of the child to the mobile phone and/or the bracelet of the parent through the GRPS wireless communication network.
The detection method for child quilt kicking sets the monitoring area in the video or image, and judges that the child does not cover the quilt well when the preset pattern appears in the set monitoring area
The detection method of the invention has lower requirement on the surrounding environment, can identify in the night vision environment without worrying about color conflict, has high detection accuracy,
[ description of the drawings ]
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a schematic block diagram of a kicked-detected apparatus of the present invention.
Fig. 2 is a flowchart of a method for detecting a child kicked by the present invention.
Fig. 3 is a schematic block diagram of a child kick detection apparatus according to embodiments 1 and 2 of the present invention.
Fig. 4 is a schematic diagram of a deep neural network according to embodiment 1 of the present invention.
Fig. 5 is a flowchart of a method for detecting a child kicked in embodiments 1 and 2 of the present invention.
Fig. 6 is a schematic diagram of a deep neural network according to embodiment 2 of the present invention.
FIG. 7 is a schematic diagram of a specific pattern according to example 2 of the present invention.
Fig. 8 is a schematic block diagram of a child kick detection apparatus according to embodiments 3 and 4 of the present invention.
Fig. 9 is a flowchart of a method for detecting a child kicked in embodiments 3 and 4 of the present invention.
Fig. 10 is a schematic block diagram of a child kicking detection apparatus according to embodiments 5 and 6 of the present invention.
Fig. 11 is a flowchart of a method for detecting a child kicked in embodiments 5 and 6 of the present invention.
Fig. 12 is a schematic view of a pajama according to the method for detecting kicking of children.
Fig. 13 is a schematic view of an abdominal belt of a child kicking detection method according to an embodiment of the present invention.
Detailed description of the preferred embodiments
The detection device and the detection method for the child quilt kicking are shown in figures 1 and 2, the detection device comprises an image acquisition module and an image analysis module, the image analysis module receives a video or an image of the image acquisition module, identifies whether a preset pattern exists in the video or the image, judges that the child quilt is not covered well if the preset pattern is detected, and judges that the child quilt is in a normal state if the preset pattern is not detected.
The children sleep wearing the pajamas with the preset patterns, the preset patterns can be directly printed on the clothes of the children or can be arranged separately from the clothes, and the patterns and the clothes are fixedly connected through inlaying or sewing or bonding or bayonets or zippers.
The algorithm used by the image analysis module can be one of R-CNN, FastR-CNN, FasterR-CNN, FPN, YOLO, SSD, RetinaNet, DenseBox, RRCfraction, DeformableCNN, CNN, RNN, inclusion, Xception, MobileNet, ResNeXt, DenseNet, SqueezeNet, ShuffleNet, SKNet and SENet based on the deep neural network technology, and can also be one of Cascade, HOG/DPM and Haar/SVM target detection algorithms.
Example 1:
as shown in fig. 3, the image acquisition module hardware adopts a camera, a haisi Hi3516cv500 or Hi3516dv300 chip is used in the camera, and the image analysis module uses the chip to perform operations.
The recognition algorithm of the image analysis module uses the MobileNetv3 photo classification neural network as shown in fig. 4. The network is trained so that the network can recognize a specified pattern or patterns. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. The network is a classification network, and children are considered not to be covered by quilts as long as the pattern appears in the picture.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing the pajamas shown in fig. 12 printed with one of the designated patterns or the abdominal belt shown in fig. 13 printed with one of the designated patterns or the sticker of one of the designated patterns capable of being stuck on the pajamas.
As shown in fig. 5, when the image captured by the camera has a designated pattern, it is considered that the child is not covered with the quilt. The image analysis module sends the information that children played the quilt to parent's cell-phone and/or bracelet through wireless communication module, and this information passes through in WIFI network or bluetooth signal transmission parent's cell-phone and/or the bracelet, cell-phone and/or bracelet vibration to remind the parent to go to cover the quilt for children.
Example 2:
as shown in fig. 3, the image acquisition module hardware adopts a camera, a haisi Hi3516cv500 or Hi3516dv300 chip is used in the camera, and the image analysis module uses the chip to perform operations.
The recognition algorithm of the image analysis module adopts a Yolov4tiny target detection neural network shown in FIG. 6. The network is trained so that it can recognize a specified pattern or patterns and can unambiguously recognize the position (upper left and lower right coordinates) of the pattern on the picture. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. When the specified pattern appears in the image, the child is considered not to cover the quilt; further, a monitoring area (e.g., a bed area) may be set in the video or image, and a child may be considered to be not well covered when the designated pattern appears in the designated area.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing a pajama printed with one of the designated patterns or a belly band printed with one of the designated patterns or a sticker of one of the designated patterns capable of being stuck on the pajama.
As shown in fig. 5, the video or the image captured by the camera is analyzed, when the image analysis module determines that the child is not covered by the child image analysis module, the child kicking information is sent to the parent's mobile phone and/or the bracelet through the wireless communication module, the information is transmitted to the parent's mobile phone and/or the bracelet through the WIFI network or the bluetooth signal, and the mobile phone and/or the bracelet vibrates, so that the parent is reminded to cover the child with the quilt.
The embodiment adopts an object detection algorithm, and the detected pattern and the position of the picture where the pattern is located are output by the algorithm. As shown in fig. 7, the picture includes 4 patterns, and the 4 patterns have 3 different patterns, and the three patterns are predefined as 1, 2, and 3 in the algorithm model. (x1, y1) (x2, y2) are the left and right bottom corner coordinates of the first row first column pattern in the picture, respectively; (x3, y3) (x4, y4) are the left and right bottom corner coordinates of the first row second column pattern in the picture, respectively; (x5, y5) (x6, y6) are the left and right bottom corner coordinates of the second row first column pattern in the picture, respectively; (x7, y7) (x8, y8) are the left and right bottom corner coordinates of the second row second column pattern in the picture, respectively. Then the algorithmic model identifies the output of the picture as (1, x1, y1, x2, y2) (2, x3, y3, x4, y4) (3, x5, y5, x6, y6) (1, x7, y7, x8, y 8).
If there are one or more pattern results specified in the camera output, it can be considered to be undercooked.
Example 3:
as shown in fig. 8, the image acquisition module hardware adopts a camera; the image analysis module is located in hardware and is an edge calculation box (such as Firefoy EC-A3399C), and the image analysis module uses the edge calculation box to perform operation.
The recognition algorithm of the image analysis module uses the MobileNetv3 photo classification neural network as shown in fig. 4. The network is trained so that the network can recognize a specified pattern or patterns. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. The network is a sort network, that is, as long as the pattern appears in the picture, it is considered that the child does not cover the quilt.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing a pajama printed with one of the designated patterns or a belly band printed with one of the designated patterns or a sticker of one of the designated patterns capable of being stuck on the pajama.
As shown in fig. 9, the camera transmits the captured video to the edge computing box, the image analysis module analyzes the video by using the computing power of the edge computing box, when the image analysis module determines that the child is not covered by the image analysis module, the image analysis module transmits information of kicking of the child to the parent's mobile phone and/or the bracelet through the wireless communication module, the information is transmitted to the parent's mobile phone and/or the bracelet through the WIFI network or the bluetooth signal, and the mobile phone and/or the bracelet vibrates, so that the parent is reminded to cover the child with the quilt.
Example 4:
as shown in fig. 8, the image acquisition module hardware adopts a camera; the image analysis module is located in hardware and is an edge calculation box (such as Firefoy EC-A3399C), and the image analysis module uses the edge calculation box to perform operation.
The recognition algorithm of the image analysis module employs a YOLOv4tiny target detection neural network as shown in fig. 6. The network is trained so that the network can recognize a specified pattern or patterns and can unambiguously recognize the position of the pattern on the picture. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. When the specified pattern appears in the image, the child is considered not to cover the quilt; further, a monitoring area (such as a bed area) can be set in the video, and when the specified pattern is in the specified area, the child is considered not to be covered by the quilt.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing a pajama printed with one of the designated patterns or a belly band printed with one of the designated patterns or a sticker of one of the designated patterns capable of being stuck on the pajama.
As shown in fig. 9, the camera transmits the captured video to the edge computing box, the image analysis module analyzes the video by using the computing power of the edge computing box, when the image analysis module determines that the child is not covered by the image analysis module, the image analysis module transmits information of kicking of the child to the parent's mobile phone and/or the bracelet through the wireless communication module, the information is transmitted to the parent's mobile phone and/or the bracelet through the WIFI network or the bluetooth signal, and the mobile phone and/or the bracelet vibrates, so that the parent is reminded to cover the child with the quilt.
In the present embodiment, the target detection algorithm shown in fig. 6 is adopted, and the detected pattern type number and the position of the pattern in the picture output by the algorithm are used. As shown in fig. 7, the picture detects 4 patterns, and the 4 patterns have 3 different patterns, and the three patterns are predefined as 1, 2 and 3 in the algorithm model. (x1, y1) (x2, y2) are the left and right bottom corner coordinates of the first row first column pattern in the picture, respectively; (x3, y3) (x4, y4) are the left and right bottom corner coordinates of the first row second column pattern in the picture, respectively; (x5, y5) (x6, y6) are the left and right bottom corner coordinates of the second row first column pattern in the picture, respectively; (x7, y7) (x8, y8) are the left and right bottom corner coordinates of the second row second column pattern in the picture, respectively. Then the algorithmic model identifies the output of the picture as (1, x1, y1, x2, y2) (2, x3, y3, x4, y4) (3, x5, y5, x6, y6) (1, x7, y7, x8, y 8).
If there are one or more pattern results specified in the camera output, it can be considered to be undercooked.
Example 5:
as shown in fig. 10, the image acquisition module hardware adopts a network camera; the image analysis module is arranged on the cloud server, and the camera transmits the captured video or image to the cloud server.
The recognition algorithm of the image analysis module uses the MobileNetv3 photo classification neural network as shown in fig. 4. The network is trained so that the network can recognize a specified pattern or patterns. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. The network is a sort network, that is, as long as the pattern appears in the picture, it is considered that the child does not cover the quilt.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing a pajama printed with one of the designated patterns or a belly band printed with one of the designated patterns or a sticker of one of the designated patterns capable of being stuck on the pajama.
As shown in fig. 11, the camera transmits the captured video to the cloud server, the image analysis module analyzes the video at the cloud, and when the image analysis module determines that the child does not cover the quilt, the information is transmitted to the mobile phone and/or the bracelet of the parent through the GRPS wireless communication network, and the mobile phone and/or the bracelet vibrate, so that the parent is reminded to cover the quilt for the child.
Example 6:
as shown in fig. 9, the image acquisition module hardware adopts a network camera; the image analysis module is arranged on the cloud server, and the camera transmits the captured video or image to the cloud server.
The recognition algorithm of the image analysis module employs a YOLOv4tiny target detection neural network as shown in fig. 6. The network is trained so that it can recognize a specified pattern or patterns and can unambiguously recognize the position (upper left and lower right coordinates) of the pattern on the picture. In order to be able to identify also in a night vision environment, the network is able to identify both the colour image and the grey image of the pattern. When the specified pattern appears in the image, the child is considered not to cover the quilt; further, a monitoring area (such as a bed area) can be set in the video, and when the specified pattern is in the specified area, the child is considered not to be covered by the quilt.
The camera is placed at a proper position so as to collect video or images of the position where the child sleeps.
The child sleeps wearing a pajama printed with one of the designated patterns or a belly band printed with one of the designated patterns or a sticker of one of the designated patterns capable of being stuck on the pajama.
As shown in fig. 10, the camera transmits the captured video to the cloud, the image analysis module analyzes the video at the cloud, and when the image analysis module determines that the child does not cover the quilt, the information is transmitted to the mobile phone and/or the bracelet of the parent through the GRPS wireless communication network, and the mobile phone and/or the bracelet vibrates, so that the parent is reminded to go to cover the quilt for the child.
In the target detection algorithm adopted in this embodiment, as shown in fig. 6, the detected pattern type number and the position of the pattern in the picture are output by the algorithm. As shown in the above figure, the picture detects 4 patterns, and the 4 patterns have 3 different patterns, and the three patterns are predefined as 1, 2 and 3 in the algorithm model. (x1, y1) (x2, y2) are the left and right bottom corner coordinates of the first row first column pattern in the picture, respectively; (x3, y3) (x4, y4) are the left and right bottom corner coordinates of the first row second column pattern in the picture, respectively; (x5, y5) (x6, y6) are the left and right bottom corner coordinates of the second row first column pattern in the picture, respectively; (x7, y7) (x8, y8) are the left and right bottom corner coordinates of the second row second column pattern in the picture, respectively. Then the algorithmic model identifies the output of the picture as (1, x1, y1, x2, y2) (2, x3, y3, x4, y4) (3, x5, y5, x6, y6) (1, x7, y7, x8, y 8).
If there are one or more pattern results specified in the output, the quilt can be considered to be disqualified.
The above embodiment of the invention has the following beneficial effects:
the detection of the kicked quilt of the child can be realized only by using the camera without adding a sensing device.
The kicking detection can be achieved in a targeted mode according to the embodiment of the invention in different seasons. When the weather is cold, the child can sleep by wearing the pajamas with the preset patterns all over the body, so that parents can be reminded even if the arms are exposed; when the weather is hot, only the abdomen is required to be provided with the preset pattern, so that parents can be reminded only when the abdomen of the child is not covered.
3> the pattern is made without special materials.
4> the recognition of the pattern is much easier to implement than the recognition of a child kicking an action.
And 5, the requirement on the surrounding environment is low, and the problem of color conflict is not needed to be worried about. And can be identified in a night vision environment.
Claims (10)
1. The utility model provides a detection device that child played quilt, includes image acquisition module and image analysis module, its characterized in that, image analysis module receives image acquisition module's video or image, whether discerns and whether has preset pattern in video or the image, if detects preset pattern, then judges that child's quilt is not covered well.
2. The device for detecting the kicked quilt of a child as claimed in claim 1, comprising a wireless communication module, wherein when the image analysis module detects the preset pattern, the wireless communication module sends the information of the kicked quilt of the child to the outside.
3. The apparatus of claim 1, wherein the image analysis module is disposed in an edge computing box, and the image analysis module operates using the edge computing box.
4. A child's kicking detection apparatus according to claim 1, including a pajama and/or an abdominal belt worn by the child, the pajama and/or the abdominal belt including at least one of said predetermined patterns.
5. The device for detecting the kicking of a child as claimed in claim 4, wherein the predetermined pattern is provided separately from the pajamas and/or the abdominal belt, and the pattern is connected with the pajamas and/or the abdominal belt by embedding or sewing or adhesion or bayonet fixing.
6. A method for detecting a child kicked quilt, comprising the apparatus for detecting a child kicked quilt of claim 1, wherein the detection process comprises the steps of:
the pajamas and/or the bellyband worn by the children comprise at least one preset pattern;
an image acquisition module of the detection device acquires a video or an image of a position where a child sleeps;
an image analysis module of the detection device receives the video or the image of the image acquisition module and identifies whether a preset pattern exists in the video or the image;
if the image analysis module detects the preset pattern, the child is judged not to be well covered.
7. The method for detecting a child kicked quilt according to claim 6, wherein the detecting device includes a wireless communication module, and in step 604, if the image analysis module detects the preset pattern, the image analysis module sends a message of the child kicking quilt to a parent's mobile phone and/or a bracelet through the wireless communication module, and the mobile phone and/or the bracelet gives an alarm after receiving the message of the child kicking quilt.
8. The method for detecting kicks of children as claimed in claim 6, wherein in step 601, the pajamas and/or the abdominal belts worn by the children include a plurality of predetermined patterns; the recognition algorithm of the image analysis module adopts a target detection neural network and/or a classification neural network; training a target detection neural network and/or a classification neural network to enable the neural network to recognize various preset patterns; in step 604, if the image analysis module detects one or more of the preset patterns, the image analysis module sends information about the child kicking the quilt to the parent's mobile phone and/or bracelet through the wireless communication module.
9. The method for detecting the child kicked quilt according to claim 6, wherein the image acquisition module is a web camera, the image analysis module is arranged on a cloud server, and the camera transmits the captured video or image to the cloud server; and the image analysis module of the cloud server sends the information of kicking the quilt of the child to the mobile phone and/or the bracelet of the parent through the GRPS wireless communication network.
10. The method for detecting a child kicked quilt according to claim 6, wherein a monitoring area is set in the video or image, and when a preset pattern appears in the set monitoring area, it is determined that the child does not cover the quilt.
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