CN113655002A - Recycled aggregate quality detection system with mortar on surface based on hyperspectral technology - Google Patents
Recycled aggregate quality detection system with mortar on surface based on hyperspectral technology Download PDFInfo
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- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/17—Systems in which incident light is modified in accordance with the properties of the material investigated
- G01N21/25—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands
- G01N21/27—Colour; Spectral properties, i.e. comparison of effect of material on the light at two or more different wavelengths or wavelength bands using photo-electric detection ; circuits for computing concentration
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Abstract
The invention provides a system for detecting the quality of recycled aggregate with mortar on the surface based on a hyperspectral technology, wherein a recycled aggregate conveying module is used for conveying recycled aggregate to an acquisition area of a hyperspectral image acquisition module; the light source module provides illumination for the acquisition area of the hyperspectral image acquisition module; the hyperspectral image acquisition module is used for acquiring original data of the spectral reflection intensity of the recycled aggregate, converting the original data into a pseudo-color image and outputting the pseudo-color image to the deep learning module; the deep learning module selects samples by using a KS algorithm, the extracted features are used for training the model, and the trained model is used for online detection; the image processing module provides parameter input for the regression analysis module; the regression analysis module constructs a regression model relating to the water absorption and apparent density of the recycled aggregate.
Description
Technical Field
The invention relates to the field of quality grade detection of mortar contained on the surface of a recycled aggregate, in particular to a system for detecting the quality of the recycled aggregate with mortar contained on the surface based on a hyperspectral technology.
Background
With the continuous investment of our country to capital construction, the rapidly increasing aggregate usage leads to the serious shortage of primary aggregates in some places, on the other hand, our country can produce a large amount of construction waste every year, the storage land is short, which leads to the environmental pollution, and the waste concrete is the main component of the construction waste. Mortar is inevitably remained on the surface of the recycled aggregate in the surface strengthening and crushing processing of the waste concrete, and the existence and the content of the mortar have serious influence on the quality of the recycled aggregate, so that the quality grade of the recycled aggregate is detected, and whether the quality of the recycled aggregate meets the recycling requirement is judged, thereby being used for producing the concrete. Developing a set of high-efficiency and high-quality recycled aggregate quality grade detection system has great significance for accelerating the utilization rate and the utilization degree of recycled aggregates in China.
Disclosure of Invention
The invention aims to solve the main technical problem of providing a system for detecting the quality of recycled aggregate with mortar on the surface based on a hyperspectral technology, which can realize the judgment of the quality grade of the recycled aggregate, thereby controlling the production process of the recycled aggregate and solving the problems of construction waste treatment and shortage of primary aggregate.
In order to solve the technical problems, the invention provides a system for detecting the quality of recycled aggregate with mortar on the surface based on a hyperspectral technology, which comprises: the system comprises a recycled aggregate conveying module, a light source module, a hyperspectral image acquisition module, a deep learning module, an image processing module and a regression analysis module;
the recycled aggregate conveying module is used for conveying recycled aggregates to an acquisition area of the hyperspectral image acquisition module;
the light source module provides illumination for the acquisition area of the hyperspectral image acquisition module;
the hyperspectral image acquisition module is used for acquiring original data of the spectral reflection intensity of the recycled aggregate, converting the original data into a pseudo-color image and outputting the pseudo-color image;
the deep learning module selects a sample by using a KS algorithm, further differentiates hyperspectral data characteristics of the sample by preprocessing data, then performs dimensionality reduction on the preprocessed sample data characteristics, finally uses the extracted characteristics for model training, and uses the trained model for online detection;
different objects detected by the trained model on line are output in the form of pictures, the image processing module carries out statistics on the number of pixel points of the different objects on the output classified pictures, then the pixel proportion of the mortar is counted, the area size and the convex hull ratio of the recycled aggregate are calculated, and the regression analysis module is provided with parameter input;
the regression analysis module constructs a regression model relating to the water absorption and apparent density of the recycled aggregate.
In a preferred embodiment: the recycled aggregate conveying module comprises a vibration dispersion feeding device, an encoder and a conveying belt; the vibration dispersion feeding device is used for providing stable and dispersed recycled aggregate for the conveyor belt; the encoder is used for reading the speed of the current conveyor belt; and the conveyor belt device conveys the recycled aggregate dispersed by the vibration dispersing device to an acquisition area of the hyperspectral image acquisition module.
In a preferred embodiment: the light source module includes two halogen lamps.
In a preferred embodiment: the hyperspectral image acquisition module is a hyperspectral camera.
In a preferred embodiment: the deep learning module comprises a light sample selection module, a data preprocessing module, a feature extraction module, a model training module and an online detection module;
the sample selection module is used for screening the collected hyperspectral data and selecting a sample; the data preprocessing module is used for further expanding the selected sample characteristics, so that the characteristic difference of the sample is more obvious; the feature extraction module performs data dimension reduction on the hyperspectral data and removes features with larger relevance; the model training module trains the extracted features so as to obtain a model with strong generalization ability and obtain model parameters for online detection; and the on-line detection module is used for using the trained model parameters for on-line classification of the recycled aggregate and outputting the classification result by using a picture.
In a preferred embodiment: the image processing module comprises image preprocessing and characteristic parameter extraction;
the image preprocessing is used for removing noise points of the classified images by Gaussian filtering and removing smaller particles in the images; and the characteristic extraction is used for counting mortar and primary aggregate pixels in the classified images, calculating the proportion of the mortar pixels and calculating the area and convex hull ratio of the recycled aggregate.
In a preferred embodiment: the regression analysis module comprises parameter input and a regression analysis model acquisition;
inputting the area ratio of the mortar, the area ratio of the recycled aggregate and the convex hull ratio of the recycled aggregate into a regression model by the parameter input; the regression analysis model is obtained by performing multiple regression on the area ratio of the mortar, the area of the recycled aggregate, the convex hull ratio of the recycled aggregate and the like, the water absorption rate and the apparent density, so as to obtain regression parameters and construct a regression equation for detecting the quality grade of the recycled aggregate.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a schematic diagram of a device of a system for detecting the quality of recycled aggregate with mortar on the surface based on hyperspectral technology.
FIG. 2 is an overall flow chart of the system for detecting the quality of the recycled aggregate with mortar on the surface based on the hyperspectral technology.
FIG. 3 is a detailed flow chart of the system for detecting the quality of the recycled aggregate with mortar on the surface based on the hyperspectral technology.
Detailed Description
The technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention; it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by those skilled in the art without any inventive work are within the scope of the present invention.
In the description of the present invention, it should be noted that the terms "upper", "lower", "inner", "outer", "top/bottom", and the like indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplification of description, but do not indicate or imply that the referred device or element must have a specific orientation, be constructed in a specific orientation, and be operated, and thus should not be construed as limiting the present invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "disposed," "sleeved/connected," "connected," and the like, are used in a broad sense, and for example, "connected" may be a wall-mounted connection, a detachable connection, an integral connection, a mechanical connection, an electrical connection, a direct connection, an indirect connection through an intermediate medium, and a communication between two elements, and those skilled in the art will understand the specific meaning of the terms in the present invention specifically.
Referring to fig. 1 to 3, the present embodiment provides a system for detecting quality of recycled aggregate with mortar on the surface based on hyperspectral technology, including: the system comprises a recycled aggregate conveying module 10, a light source module 20, a hyperspectral image acquisition module 30, a deep learning module 40, an image processing module 50 and a regression analysis module 60;
the recycled aggregate conveying module 10 is used for conveying recycled aggregates to an acquisition area of the hyperspectral image acquisition module 30;
the light source module 20 provides illumination for the acquisition area of the hyperspectral image acquisition module 30;
the hyperspectral image acquisition module 30 is configured to acquire original data of the spectral reflection intensity of the recycled aggregate, convert the original data into a pseudo-color image, and output the pseudo-color image;
the deep learning module 40 selects a sample by using a KS algorithm, further differentiates hyperspectral data characteristics of the sample by preprocessing data, then performs dimensionality reduction on the preprocessed sample data characteristics, finally uses the extracted characteristics for model training, and uses the trained model for online detection;
different objects detected by the trained model on line are output in the form of pictures, the image processing module 50 counts the number of pixel points of the different objects on the output classified pictures, then counts the pixel proportion of the mortar, calculates the area size and convex hull ratio of the recycled aggregate, and provides parameter input for the regression analysis module 60;
the regression analysis module 60 constructs a regression model relating to the water absorption and apparent density of the recycled aggregate.
The work flow of the recycled aggregate conveying module is shown in fig. 1, the speed of the conveyor belt is controlled by the controller to convey the recycled aggregate placed on the conveyor belt to the data acquisition area of the hyperspectral image acquisition module, and the encoder reads the current speed of the conveyor belt.
The working flow of the light source module is as shown in fig. 1, two 50W halogen lamps are symmetrically distributed on two sides of the hyperspectral camera, so that a stable and uniform ideal illumination environment is provided for a measurement area of the hyperspectral camera, and the influence of the light source on acquired spectral data is reduced as much as possible.
In this embodiment, the recycled aggregate conveying module 10 includes a vibration dispersion feeding device 11, an encoder 12, and a conveyor belt 13; the vibration dispersion feeding device 11 is used for providing stable and dispersed recycled aggregate for the conveyor belt 13; the encoder 12 is used for reading the current speed of the conveyor belt 13; the conveyor belt device 13 conveys the recycled aggregate dispersed by the vibration dispersing device to the collection area of the hyperspectral image collection module 30.
The light source module 20 includes two halogen lamps 21.
The hyperspectral image acquisition module 30 is a hyperspectral camera 31.
The deep learning module 40 comprises a light sample selection module 41, a data preprocessing module 42, a feature extraction module 43, a model training module 44 and an online detection module 45;
the sample selection module 41 is used for screening the collected hyperspectral data and selecting a sample; the data preprocessing module 42 further expands the selected sample characteristics, so that the characteristic difference of the sample is more obvious; the feature extraction module 43 performs data dimension reduction on the hyperspectral data, and removes features with large relevance; the model training module 44 trains the extracted features, so as to obtain a model with strong generalization ability and obtain model parameters for online detection; the on-line detection module 45 uses the trained model parameters for on-line classification of the recycled aggregate, and outputs the classification result by using a picture.
The image processing module 50 comprises image preprocessing 51 and characteristic parameter extraction 52;
the image preprocessing 51 removes noise of the classified images by using gaussian filtering to remove smaller particles in the images; the feature extraction 52 performs statistics on mortar and primary aggregate pixels in the classified images, calculates the proportion of the mortar pixels, and calculates the area and convex hull ratio of the recycled aggregate. The feature extraction 52 finds the edge contour of the recycled aggregate after the binarization of the image, then counts the number of pixel points of the mortar and the primary aggregate in the contour of the original image respectively, and the area ratio of the mortar can be obtained by comparing the total number of the pixel points of the mortar with the number of the pixel points of the whole recycled aggregate. In addition, the area, convex hull ratio, etc. of the recycled aggregate were also calculated.
The regression analysis module 60 comprises a parameter input 61 and an acquisition regression analysis model 62;
the parameter input 61 inputs the area ratio of the mortar, the area ratio of the recycled aggregate and the convex hull ratio of the recycled aggregate into the regression model; the regression analysis model 62 is used for performing multiple regression on the area ratio of the mortar, the area of the recycled aggregate, the convex hull ratio of the recycled aggregate and the like, the water absorption rate and the apparent density, so as to obtain regression parameters and construct a regression equation for detecting the quality grade of the recycled aggregate.
The mounting mode and the work flow chart of the hyperspectral image acquisition module are shown in figures 1 and 3, the hyperspectral camera acquires the illumination intensity reflected on the surface of the recycled aggregate through a line scanning surface, so that hyperspectral data are acquired, meanwhile, the conveyor belt drives the recycled aggregate to move, so that the surface illumination intensity of the whole recycled aggregate is acquired, and then the corresponding relation between stereoscopic vision systems is calculated, so that a pseudo-color image of the recycled aggregate is reconstructed.
The deep learning module workflow diagram is shown in fig. 3, and a KS algorithm is used for screening collected hyperspectral data to select representative samples. The data preprocessing is to select a preprocessing method with the best effect on the differentiation of the hyperspectral data characteristics according to the differentiation degree of the selected hyperspectral data characteristics by comparing methods such as first-order differentiation, logarithmic transformation of derivative, envelope elimination and the like. The feature extraction module uses principal component analysis PCA and wavelet transformation WT to extract features of the preprocessed hyperspectral data, so that redundant data can be removed, the obtained features are compared and used for the training effect of the model, and a feature extraction method with the best model training effect is selected. Model training selects the best training model for online detection by comparing the training model effects of the extreme learning machine, the Gaussian kernel extreme learning machine and the wavelet kernel extreme learning machine. And the online detection is used for online detection and classification of the recycled aggregate by using the obtained model with the best generalization capability.
The work flow chart of the image processing module is as shown in fig. 3, and the classified images are subjected to gaussian filtering to remove noise, and small points in the images are removed. The extraction of the characteristic parameters is realized by finding the edge contour of the recycled aggregate after the binarization of the image, then respectively counting the number of pixel points of the mortar and the primary aggregate in the contour of the original image, and comparing the total number of the pixel points of the mortar with the number of the pixel points of the whole recycled aggregate to obtain the area ratio of the mortar. In addition, the area, convex hull ratio, etc. of the recycled aggregate were also calculated.
The work flow diagram of the regression analysis module is shown in fig. 3, and the parameter inputs include the mortar area ratio in the recycled aggregate, the area of the recycled aggregate, the convex hull ratio of the recycled aggregate, and the like. And the regression analysis model performs multiple regression on the area ratio of the mortar in the recycled aggregate, the area of the recycled aggregate, the convex hull ratio of the recycled aggregate and the like, the water absorption rate and the apparent density of the recycled aggregate to obtain a multiple regression equation, so that the quality grade of the recycled aggregate is detected on line.
The aggregate conveying module comprises a vibration dispersing device, a conveyor belt device and an encoder device. The controller device controls the conveying speed of the conveying belt, the conveying belt device sequentially conveys the recycled aggregate dispersed by the vibration dispersing device to each image acquisition area, and the encoder device reads the speed of the current conveying belt.
The light source module utilizes the halogen lamps with the power of 50W, the halogen lamps are symmetrically distributed on the two sides of the hyperspectral camera, a stable and uniform ideal illumination environment is provided for a measurement area of the hyperspectral camera, and the influence of the light source on acquired spectral data is reduced as much as possible.
The hyperspectral image acquisition module is characterized in that a hyperspectral camera acquires illumination intensity reflected on the surface of the recycled aggregate through a line scanning surface, so that hyperspectral data are acquired, meanwhile, a conveyor belt drives the recycled aggregate to move, so that the surface illumination intensity of the whole recycled aggregate is acquired, and then the corresponding relation between stereoscopic vision systems is calculated, so that a pseudo-color image of the recycled aggregate is reconstructed.
On the basis of the technical scheme, the deep learning module further comprises the steps of sample selection, data preprocessing, feature extraction, model training and online detection. And the sample selection uses a KS algorithm to screen the collected hyperspectral data, and a representative sample is selected. The data preprocessing is a preprocessing method which selects the best effect on the differentiation of the hyperspectral data characteristics according to the differentiation degree of the selected hyperspectral data characteristics by comparing methods such as first-order differentiation, logarithmic transformation of derivatives, envelope elimination and the like.
The above description is only a preferred embodiment of the present invention, but the design concept of the present invention is not limited thereto, and any person skilled in the art can make insubstantial changes in the technical scope of the present invention within the technical scope of the present invention, and the actions infringe the protection scope of the present invention are included in the present invention.
Claims (7)
1. A system for detecting the quality of recycled aggregate with mortar on the surface based on hyperspectral technology is characterized by comprising: the system comprises a recycled aggregate conveying module (10), a light source module (20), a hyperspectral image acquisition module (30), a deep learning module (40), an image processing module (50) and a regression analysis module (60);
the recycled aggregate conveying module (10) is used for conveying recycled aggregates to an acquisition area of the hyperspectral image acquisition module (30);
the light source module (20) provides illumination for the acquisition area of the hyperspectral image acquisition module (30);
the hyperspectral image acquisition module (30) is used for acquiring original data of the spectral reflection intensity of the recycled aggregate, converting the original data into a pseudo-color image and outputting the pseudo-color image to the deep learning module (40);
the deep learning module (40) selects a sample by using a KS algorithm, further differentiates hyperspectral data characteristics of the sample by preprocessing data, then performs dimensionality reduction processing on the preprocessed sample data characteristics, finally uses the extracted characteristics for model training, and uses the trained model for online detection;
different objects detected by the trained model on line are output in the form of pictures, the image processing module (50) carries out statistics on the number of pixel points of the different objects on the output classified pictures, then the pixel proportion of mortar is counted, the area size and the convex hull ratio of the recycled aggregate are calculated, and the regression analysis module (60) is provided with parameter input;
the regression analysis module (60) constructs a regression model relating to the water absorption and apparent density of the recycled aggregate.
2. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface, according to claim 1, is characterized in that: the recycled aggregate conveying module (10) comprises a vibration dispersion feeding device (11), an encoder (12) and a conveying belt (13); the vibration dispersion feeding device (11) is used for providing stable and dispersed recycled aggregate for the conveyor belt (13); the encoder (12) is used for reading the speed of the current conveyor belt (13); the conveyor belt device (13) conveys the recycled aggregate dispersed by the vibration dispersing device to an acquisition area of the hyperspectral image acquisition module (30).
3. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface, according to claim 2, is characterized in that: the light source module (20) includes two halogen lamps (21).
4. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface, according to claim 3, is characterized in that: the hyperspectral image acquisition module (30) is a hyperspectral camera (31).
5. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface according to claim 4, wherein: the deep learning module (40) comprises a light sample selecting module (41), a data preprocessing module (42), a feature extraction module (43), a model training module (44) and an online detection module (45);
the sample selection module (41) is used for screening the collected hyperspectral data and selecting a sample; the data preprocessing module (42) is used for further expanding the selected sample characteristics, so that the characteristic difference of the sample is more obvious; the feature extraction module (43) performs data dimension reduction on the hyperspectral data, and removes features with large relevance; the model training module (44) trains the extracted features so as to obtain a model with strong generalization ability and obtain model parameters for online detection; and the on-line detection module (45) uses the trained model parameters for on-line classification of the recycled aggregate, and outputs the classification result by using a picture.
6. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface, according to claim 5, is characterized in that: the image processing module (50) comprises image preprocessing (51) and characteristic parameter extraction (52);
the image preprocessing (51) removes noise points of the classified images by Gaussian filtering and removes smaller particles in the images; and the characteristic extraction (52) is used for counting the mortar and primary aggregate pixels in the classified images, calculating the proportion of the mortar pixels and calculating the area and convex hull ratio of the recycled aggregate.
7. The high spectrum technology-based system for detecting the quality of the recycled aggregate with mortar on the surface, according to claim 6, is characterized in that: the regression analysis module (60) comprises a parameter input (61) and an acquisition regression analysis model (62);
inputting the area ratio of the mortar, the area ratio of the recycled aggregate and the convex hull ratio of the recycled aggregate into a regression model by the parameter input (61); the obtained regression analysis model (62) performs multiple regression on the area ratio of the mortar, the area of the recycled aggregate, the convex hull ratio of the recycled aggregate and the like, the water absorption rate and the apparent density, so as to obtain regression parameters and construct a regression equation for detecting the quality grade of the recycled aggregate.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757948A (en) * | 2022-06-14 | 2022-07-15 | 福建南方路面机械股份有限公司 | Deep learning-based method and device for detecting content of recycled aggregate mortar |
| CN116689133A (en) * | 2023-08-04 | 2023-09-05 | 福建南方路面机械股份有限公司 | Deep learning-based recycled aggregate quality control method and device and readable medium |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2952551A1 (en) * | 2016-11-30 | 2018-05-30 | National Research Council Of Canada | Method and system for characterizing an aggregate sample by using laser-induced breakdown spectroscopy |
| US20180247153A1 (en) * | 2017-02-28 | 2018-08-30 | Verily Life Sciences Llc | System and method for multiclass classification of images using a programmable light source |
| US20180365822A1 (en) * | 2017-06-19 | 2018-12-20 | ImpactVision, Inc. | System and method for hyperspectral image processing to identify foreign object |
| CN109145992A (en) * | 2018-08-27 | 2019-01-04 | 西安电子科技大学 | Cooperation generates confrontation network and sky composes united hyperspectral image classification method |
| CN110282926A (en) * | 2019-06-26 | 2019-09-27 | 内蒙古巨华集团大华建筑安装有限公司 | A kind of building waste materials recycling concrete and preparation method thereof |
| CN110458202A (en) * | 2019-07-18 | 2019-11-15 | 东莞弓叶互联科技有限公司 | Image processing method, device, equipment and computer readable storage medium |
| CN111062078A (en) * | 2019-12-18 | 2020-04-24 | 南京航空航天大学 | Quality grading method suitable for mixed type recycled coarse aggregate |
| CN111368712A (en) * | 2020-03-02 | 2020-07-03 | 四川九洲电器集团有限责任公司 | Hyperspectral image disguised target detection method based on deep learning |
| CN111751253A (en) * | 2020-07-06 | 2020-10-09 | 重庆理工大学 | Forming method and quality detection method of concrete aggregate detection model |
| CN111754463A (en) * | 2020-06-02 | 2020-10-09 | 石家庄铁道大学 | Ballastless track CA mortar layer defect detection method based on convolutional neural network |
| WO2021082428A1 (en) * | 2019-10-29 | 2021-05-06 | 平安科技（深圳）有限公司 | Semi-supervised learning-based image classification method and apparatus, and computer device |
| CN113177909A (en) * | 2021-04-01 | 2021-07-27 | 华侨大学 | Multi-mode visual detection method and system for recycled aggregate with mortar on surface |
-
2021
- 2021-08-30 CN CN202111005886.5A patent/CN113655002B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2952551A1 (en) * | 2016-11-30 | 2018-05-30 | National Research Council Of Canada | Method and system for characterizing an aggregate sample by using laser-induced breakdown spectroscopy |
| US20180247153A1 (en) * | 2017-02-28 | 2018-08-30 | Verily Life Sciences Llc | System and method for multiclass classification of images using a programmable light source |
| US20180365822A1 (en) * | 2017-06-19 | 2018-12-20 | ImpactVision, Inc. | System and method for hyperspectral image processing to identify foreign object |
| CN109145992A (en) * | 2018-08-27 | 2019-01-04 | 西安电子科技大学 | Cooperation generates confrontation network and sky composes united hyperspectral image classification method |
| CN110282926A (en) * | 2019-06-26 | 2019-09-27 | 内蒙古巨华集团大华建筑安装有限公司 | A kind of building waste materials recycling concrete and preparation method thereof |
| CN110458202A (en) * | 2019-07-18 | 2019-11-15 | 东莞弓叶互联科技有限公司 | Image processing method, device, equipment and computer readable storage medium |
| WO2021082428A1 (en) * | 2019-10-29 | 2021-05-06 | 平安科技（深圳）有限公司 | Semi-supervised learning-based image classification method and apparatus, and computer device |
| CN111062078A (en) * | 2019-12-18 | 2020-04-24 | 南京航空航天大学 | Quality grading method suitable for mixed type recycled coarse aggregate |
| CN111368712A (en) * | 2020-03-02 | 2020-07-03 | 四川九洲电器集团有限责任公司 | Hyperspectral image disguised target detection method based on deep learning |
| CN111754463A (en) * | 2020-06-02 | 2020-10-09 | 石家庄铁道大学 | Ballastless track CA mortar layer defect detection method based on convolutional neural network |
| CN111751253A (en) * | 2020-07-06 | 2020-10-09 | 重庆理工大学 | Forming method and quality detection method of concrete aggregate detection model |
| CN113177909A (en) * | 2021-04-01 | 2021-07-27 | 华侨大学 | Multi-mode visual detection method and system for recycled aggregate with mortar on surface |
Non-Patent Citations (2)
| Title |
|---|
| 吴瑞: "基于高光谱成像和深度学习的基于高光谱成像和深度学习的可回收垃圾分类方法研究", 中国优秀硕士学位论文全文数据库, no. 2020, pages 027 - 169 * |
| 张顺: "基于深度学习的高光谱图像目标检测及场景分类", 中国优秀硕士学位论文全文数据库, no. 2021, pages 028 - 296 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114757948A (en) * | 2022-06-14 | 2022-07-15 | 福建南方路面机械股份有限公司 | Deep learning-based method and device for detecting content of recycled aggregate mortar |
| CN116689133A (en) * | 2023-08-04 | 2023-09-05 | 福建南方路面机械股份有限公司 | Deep learning-based recycled aggregate quality control method and device and readable medium |
| CN116689133B (en) * | 2023-08-04 | 2023-12-15 | 福建南方路面机械股份有限公司 | Deep learning-based recycled aggregate quality control method and device and readable medium |
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