CN114487110A - Floor tile hollowing automatic detection method and device - Google Patents
Floor tile hollowing automatic detection method and device Download PDFInfo
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- CN114487110A CN114487110A CN202210067777.4A CN202210067777A CN114487110A CN 114487110 A CN114487110 A CN 114487110A CN 202210067777 A CN202210067777 A CN 202210067777A CN 114487110 A CN114487110 A CN 114487110A
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- 238000001514 detection method Methods 0.000 title claims abstract description 61
- 238000013135 deep learning Methods 0.000 claims abstract description 11
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- 238000000034 method Methods 0.000 claims abstract description 7
- 239000000523 sample Substances 0.000 claims description 5
- 230000003287 optical effect Effects 0.000 claims description 4
- 238000001303 quality assessment method Methods 0.000 claims description 3
- 238000011156 evaluation Methods 0.000 claims 1
- 239000000919 ceramic Substances 0.000 abstract description 9
- 238000013441 quality evaluation Methods 0.000 abstract description 5
- 238000010276 construction Methods 0.000 abstract description 4
- 238000003384 imaging method Methods 0.000 abstract description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 2
- 238000010586 diagram Methods 0.000 description 6
- 238000005259 measurement Methods 0.000 description 4
- 239000004568 cement Substances 0.000 description 3
- 238000013136 deep learning model Methods 0.000 description 3
- 238000013527 convolutional neural network Methods 0.000 description 2
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- 239000004973 liquid crystal related substance Substances 0.000 description 1
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- 238000011176 pooling Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/04—Analysing solids
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- G—PHYSICS
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
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- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4481—Neural networks
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- G—PHYSICS
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Abstract
The method comprises the steps of transmitting ultrasonic waves to a detection point of the laid floor tile, receiving echo signals from the floor tile, processing the ultrasonic signals by using a trained deep learning classification model to obtain a prediction result of the hollowing situation of the detection point, and imaging all recorded detection points corresponding to the hollowing situation on a display screen in a cloud picture mode to obtain a graphic representation of the laying quality evaluation result of the floor tile. The invention can effectively avoid the damage of a small hammer knocking detection mode to certain thinner ceramic tiles with more expensive manufacturing cost by utilizing the ultrasonic wave to detect the hollowing phenomenon of the floor tiles, has high detection speed and high accuracy, greatly reduces the hollowing phenomenon of a finished product room, and saves the fund and the construction period.
Description
Technical Field
The invention relates to a floor tile hollowing automatic detection method and device.
Background
Floor tiles are used in a large number of applications as part of modern buildings due to their aesthetic, easy to clean, and other characteristics. Hollowing is the phenomenon of an air gap layer between a tile and a substrate when the tile is poorly laid. Once spot hollowing occurs at a place where the ceramic tile is frequently trodden, the problem of large-area hollowing and even moving of the whole ceramic tile can be further caused by trodden and the like. If hollowing can be found in time just after the tile is laid, the tile is easy to replace, and the cost is low, so modern construction or decoration can be generally checked and supplemented after the tile is solidified two to three days after the tile is laid. At present, the hollowing detection method is almost completed manually, and whether hollowing occurs is judged by means of sound generated by knocking the ground or floor tiles and floors by hammers. However, this method is very inefficient, and when some thin and expensive tiles are knocked, scratches may be formed on the surfaces of the tiles, which may cause the tiles to be damaged, and only professional constructors or optometrists can make accurate judgment.
Disclosure of Invention
The invention provides a floor tile hollowing automatic detection method and device, wherein ultrasonic signals are used for obtaining hollowing information at the bottom of a tile, a deep learning model is used for processing the ultrasonic signals for hollowing judgment, the detection efficiency and the detection accuracy are high, and any damage to the floor tile cannot be caused.
According to a first aspect of embodiments of the present invention, there is provided a floor tile hollowing automatic detection method, comprising: and transmitting ultrasonic waves to a detection point of the laid floor tile, receiving an echo signal from the floor tile, and processing the ultrasonic signal by using a trained deep learning classification model to obtain a prediction result of the hollowing condition of the detection point.
According to a second aspect of embodiments of the present invention, there is provided a floor tile hollowing automatic detection device, comprising: the handheld probe is provided with an ultrasonic sensor which transmits ultrasonic waves to a detection point of a floor tile which is laid and receives echo signals from the floor tile; and the processor is used for processing the ultrasonic signals by using the trained deep learning classification model to obtain a prediction result of the empty drum situation of the detection point.
According to a third aspect of embodiments of the present invention, there is provided a floor tile hollowing automatic detection device comprising: the intelligent trolley is provided with an ultrasonic sensor which transmits ultrasonic waves to a detection point of a paved floor tile and receives an echo signal from the floor tile; and the processor is used for controlling the form of the intelligent trolley and processing the ultrasonic signals by using the trained deep learning classification model to obtain the prediction result of the empty drum situation of the detection point.
In some examples, all recorded detection points are imaged on a display screen in a cloud form corresponding to the hollowing situation, resulting in a graphical representation of the floor tile laying quality assessment results.
In some examples, an acoustic and/or optical alarm is performed when empty drum is detected.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings of the embodiments will be briefly described below.
Fig. 1 is a schematic diagram of ultrasonic measurement under different empty drum conditions according to an embodiment of the present invention.
Fig. 2 is a flowchart of detecting hollowing by traversing a room according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of detection of an S-shaped traversal room of an intelligent vehicle according to an embodiment of the present invention.
Fig. 4 is an exemplary diagram of a detection result according to an embodiment of the invention.
Detailed Description
A floor tile hollowing automatic detection method based on deep learning comprises the following steps: transmitting ultrasonic waves to detection points of the floor tiles, receiving echo signals from the floor tiles, processing the ultrasonic signals by using a trained deep learning classification model to obtain a prediction result (with or without hollowing) of hollowing conditions of the points, and imaging all recorded detection points corresponding to the hollowing conditions on a display screen in a cloud picture mode to obtain a floor tile laying quality evaluation result diagram. And guiding the constructor to re-lay the floor at the part with the hollowness according to the floor tile laying quality evaluation result diagram.
Fig. 1 is a schematic diagram of ultrasonic measurement under different hollowing conditions. The ultrasonic sensor facing the ground excites a preset string of sine pulses, ultrasonic signals pass through the air gap and penetrate through the ceramic tile, and then the ultrasonic sensor collects and sends out signals within a period of time. If the tile is well laid, namely the tile is fully contacted with the cement layer, most of the ultrasonic waves are directly transmitted because the tile and the cement layer are solid layers and the acoustic impedance difference is not large; if hollowing exists between the ceramic tile and the cement, due to the huge acoustic impedance difference between the solid and the gas, the ultrasound can generate strong reflection and echo at the ceramic tile-air interface and be captured by the receiving sensor, and the difference between the two can be reflected in the received signal, so that the deep learning model is utilized to judge.
The method is characterized in that a one-dimensional convolutional neural network is used, ultrasonic signals collected by a sensor can be directly input without preprocessing, the convolutional neural network is formed by alternately connecting a convolutional layer, a pooling layer, a batch normalization layer and a full-connection layer to perform feature extraction and mapping on the input one-dimensional signals, and then the last layer is formed by outputting two probability values respectively corresponding to empty drum and empty drum-free by a softmax layer to realize empty drum judgment.
In order to enable the deployed deep learning model to accurately judge the empty drum condition, a large number of signals corresponding to the empty drum condition and the non-empty drum condition need to be prepared in advance for training, and the signals can be obtained in an actual construction scene or a laboratory simulation scene. After data and corresponding labels are obtained, the data are input into a network model which is initialized randomly, the input of the model is compared with a real result, a cross entropy loss function and an Adam optimizer are used for carrying out back propagation and gradient updating on model parameters until a neural network is sufficiently converged, the model is stored, and training is completed.
In some examples, a floor tile hollowing automatic detection device based on deep learning is also provided. The device body can be a smart car or a handheld probe similar to a mine finder, but is not limited to the smart car. The device main body is provided with an ultrasonic sensor, a processor and a display screen. The ultrasonic sensor is used for transmitting ultrasonic waves to a floor tile detection point and receiving echo signals from the floor tile. The processor is configured to perform the steps of: and processing the ultrasonic signals by using a trained deep learning classification model to obtain a prediction result (with or without hollowing) of the hollowing situation of the point, and imaging all recorded detection points corresponding to the hollowing situation on a display screen in a cloud picture mode to obtain a floor tile laying quality evaluation result graphic representation. It should be noted that the display screen is not essential, and the alarm is given by sound and/or light when the empty drum part is detected.
When the device main body is a handheld probe, each floor tile laid is detected by manually holding the probe. When the device main body is an intelligent trolley, the intelligent trolley can be controlled in a remote control mode, and the intelligent trolley can be detected according to a planned path through a program.
As shown in fig. 2 and 3, firstly, the detection stepping of the intelligent trolley is preset, namely, the laying quality is detected once at intervals, and the detection stepping is determined according to the size of the ceramic tile, so that at least four detection points are ensured for each ceramic tile. The intelligent trolley is placed at a corner of a wall and started along the parallel direction of the wall, stops after running forwards for a detection stepping distance, carries out ultrasonic detection, and continues to run after obtaining a floor tile laying quality evaluation result at the point; when an obstacle detector on the trolley detects that the distance between an obstacle such as a front wall surface and the like and the intelligent trolley is less than one detection step, the intelligent trolley records the number of detected points in the section of straight driving process, the obstacle detector obtains the direction without the obstacle in front, records the first rotating direction as the reference direction, the trolley rotates 90 degrees towards the reference direction and then drives forwards for one detection step, then rotates 90 degrees towards the reference direction and continues to drive forwards, thus realizing the single-path detection and the corresponding turning around, continues to drive forwards straight and detect until the front meets the obstacle such as the wall surface, the door and the like, then rotates 90 degrees in the opposite direction of the reference direction, drives for one step distance, detects and rotates 90 degrees towards the opposite direction of the reference, thus the trolley achieves the position and the state which are separated by two step distances from the initial position and parallel to the driving direction, the intelligent vehicle continuously runs in an S shape in a room with floor tiles laid, and the intelligent vehicle continuously runs in the S shape until the intelligent vehicle reaches a set terminal point.
When the intelligent trolley moves and detects the empty drum condition of each point, the times, the reference rotating direction and the empty drum judgment result of each detection point measured by each straight line path are recorded in a storage device of the trolley in a data format, so that only the data need to be read, a detection grid point image is drawn according to the measurement sequence and the measurement position, each grid point represents a detected point position, the position of each grid point corresponds to the coordinate of a real detection point in a room one by one, then each point position is distinguished to have an empty drum and have no empty drum in different colors, and a final empty drum condition image is obtained after all points are drawn, which is shown in fig. 4. And controlling the intelligent trolley main control chip to display the hollowing condition image on a liquid crystal screen of the trolley for a worker to check and store.
The invention can effectively avoid the damage of a small hammer knocking detection mode to certain thinner ceramic tiles with more expensive manufacturing cost by utilizing the ultrasonic wave to detect the hollowing phenomenon of the floor tiles, has high detection speed and high accuracy, greatly reduces the hollowing phenomenon of a finished product room, saves the fund and the construction period, and realizes the full automation of the detection by using an intelligent trolley to automatically find the way.
Claims (10)
1. A floor tile hollowing automatic detection method is characterized by comprising the following steps: and transmitting ultrasonic waves to a detection point of the laid floor tile, receiving an echo signal from the floor tile, and processing the ultrasonic signal by using a trained deep learning classification model to obtain a prediction result of the hollowing condition of the detection point.
2. Method according to claim 1, characterized in that all recorded detection points are imaged on a display screen in the form of a cloud corresponding to the hollowing situation, resulting in a graphical representation of the results of the evaluation of the laying quality of the floor tiles.
3. Method according to claim 1 or 2, characterized in that an acoustic and/or optical alarm is performed when empty drums are detected.
4. The utility model provides a floor tile hollowing automatic checkout device which characterized in that includes:
the handheld probe is provided with an ultrasonic sensor which transmits ultrasonic waves to a detection point of a floor tile which is laid and receives echo signals from the floor tile; and
and the processor is used for processing the ultrasonic signals by using the trained deep learning classification model to obtain a prediction result of the empty drum situation of the detection point.
5. The apparatus according to claim 3, wherein all recorded detection points are imaged on the display screen in cloud form corresponding to the hollowing situation, resulting in a graphical representation of the results of the floor tile laying quality assessment.
6. Device according to claim 4 or 5, characterized in that an acoustic and/or optical alarm is made when empty drum is detected.
7. The utility model provides a floor tile hollowing automatic checkout device which characterized in that includes:
the intelligent trolley is provided with an ultrasonic sensor which transmits ultrasonic waves to a detection point of a paved floor tile and receives an echo signal from the floor tile; and
and the processor is used for controlling the form of the intelligent trolley and processing the ultrasonic signals by using the trained deep learning classification model to obtain the prediction result of the empty drum situation of the detection point.
8. The apparatus according to claim 7, wherein all recorded detection points are imaged on the display screen in cloud form corresponding to the hollowing situation, resulting in a graphical representation of the results of the floor tile laying quality assessment.
9. Device according to claim 7, characterized in that an acoustic and/or optical alarm is provided when empty drum is detected.
10. The device according to claim 7, 8 or 9, wherein the intelligent trolley is controlled in a manner that: starting linear driving along the wall parallel direction at the wall corner, stopping after driving forward for a detection stepping distance, carrying out ultrasonic detection on the laid floor tiles, and continuing driving after obtaining a prediction result of the hollowing condition; when an obstacle detector on the intelligent trolley detects that a front obstacle is less than one detection step away from the intelligent trolley, the intelligent trolley records the number of detected points in the section of straight driving process, the obstacle detector obtains the direction without the obstacle in front, records the first rotating direction as the reference direction, the trolley rotates 90 degrees towards the reference direction and then drives forwards for one detection step, then rotates 90 degrees towards the reference direction and continues to drive forwards, the detection of a single path and the corresponding turning around are realized, the straight driving and the detection continue to be carried out forwards until the front meets the obstacle, then rotates 90 degrees in the direction opposite to the reference direction, drives for one step distance, detects and rotates 90 degrees towards the reference direction, so that the intelligent trolley reaches the position and the state which are separated by two step distances from the initial position and parallel to the driving direction, and completing one-time complete S-shaped driving and detection, continuously repeating the actions, changing the turning direction according to the reference direction every time, and repeating the steps in such a way, so that the intelligent trolley continuously drives in an S shape in a room with floor tiles laid till the intelligent trolley reaches a set terminal point.
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| CN202210067777.4A CN114487110A (en) | 2022-01-20 | 2022-01-20 | Floor tile hollowing automatic detection method and device |
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Cited By (1)
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