CN113191271A - Method for detecting surface particulate matter of weir dam based on deep learning - Google Patents

Method for detecting surface particulate matter of weir dam based on deep learning Download PDF

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CN113191271A
CN113191271A CN202110484329.XA CN202110484329A CN113191271A CN 113191271 A CN113191271 A CN 113191271A CN 202110484329 A CN202110484329 A CN 202110484329A CN 113191271 A CN113191271 A CN 113191271A
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付兵杰
王小波
李书
栾约生
张锐
夏金梧
何林青
崔亚辉
朱云法
石纲
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CHANGJIANG INSTITUTE OF SURVEY TECHNOLOGY MINISTRY OF WATER RESOURCES
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Abstract

The invention discloses a method for detecting surface-layer particulate matter of a weir dam based on deep learning, which comprises the following steps: acquiring a color image of the surface granular substance of the weir dam, and establishing a training image data set and a test image data set; preprocessing a training image data set to form a standard training image data set; marking the surface granular substance of the damming dam in the standard training image data set to generate a marked file set; training the standard training image data set and the labeled file set based on a deep learning algorithm to generate a deep learning model; carrying out target identification on the surface particulate matter of the damming dam in the test image data set by using a deep learning model; carrying out particle size measurement and calculation on the identification target based on a three-dimensional reconstruction algorithm; and evaluating the model by adopting the identification precision and the particle size measuring and calculating precision. The method provided by the invention is used for realizing automatic detection of the surface granular substance of the weir dam, and has the characteristics of simple process, reliable calculation, high identification speed, high accuracy and strong robustness.

Description

Method for detecting surface particulate matter of weir dam based on deep learning
Technical Field
The invention relates to the technical field of emergency rescue of a damming dam, in particular to a method for detecting surface-layer particulate matters of the damming dam based on deep learning.
Background
The damming dam is a special dam body formed by water storage after intercepting valleys and rivers due to landslide, collapse, debris flow, volcanic karst flow, tillite and the like caused by rainfall, earthquake, volcano, glacier activity and the like, and has the characteristics of large dam body square amount, large water storage amount, large safety threat and the like. The structural characteristics of the formed damming dam are important data bases for stability analysis and emergency rescue. The structure of the damming dam depends on the material source, the landform shape of the river valley and the motion accumulation process, and the material composition of the damming dam generally has the characteristics of uneven soil and stone distribution, uneven particle size of the lump stones, uneven compactness and the like, so that the difficulty in effectively detecting the damming dam is increased.
At present, the detection of the surface granular substances of the weir dam mainly adopts a manual mode, the automation degree is low, the timeliness is poor, and the requirement of emergency rescue is difficult to meet. Therefore, a deep learning-based method for detecting the particulate matter on the surface layer of the weir dam needs to be researched. In recent years, with the development of convolutional neural networks and the improvement of hardware computing power, deep learning-based target detection has made a breakthrough progress and has been widely applied in various fields of computer vision, and many advanced target detection algorithms use deep learning networks as backbone networks and detection networks thereof to extract features from input images or videos. The target detection method based on deep learning comprises two types: one is a two-stage target detection method, i.e., candidate regions are generated first, and then the candidate regions are classified and regressed, such as FasterR-CNN; the other is a one-stage target detection method, i.e. the object type and coordinates are directly regressed from the image without generating a candidate frame, and representative methods are YOLO, SSD, and the like.
Disclosure of Invention
The invention aims to provide a method for detecting the surface granular substance of the weir dam based on deep learning, which is used for realizing the automatic detection of the surface granular substance of the weir dam and has the characteristics of simple process, reliable calculation, high identification speed, high accuracy and strong robustness.
In order to achieve the purpose, the invention provides the following scheme:
a method for detecting surface particulate matter of a weir dam based on deep learning comprises the following steps:
s1) a remote sensing device is used for carrying a digital camera to obtain color images of the surface layer particulate matter of the weir dam under different conditions, and a training image data set and a test image data set are established;
s2) preprocessing the training image data set to form a standard training image data set;
s3) manually labeling the surface layer particulate matter of the weir dam in the standard training image data set according to the selected label to generate a labeled file set;
s4) training the standard training image data set obtained in the step S2) and the labeled file set obtained in the step S3) based on a deep learning algorithm, and generating a deep learning model after training is completed;
s5) carrying out target identification on the surface particulate matter of the weir dam in the test image data set obtained in the step S1) by using a deep learning model, and calculating identification precision;
s6) carrying out particle size measurement and calculation on the identification target based on a three-dimensional reconstruction algorithm, and calculating the particle size measurement and calculation precision;
s7) using the two indices of the recognition accuracy calculated in step S5) and the particle diameter measurement accuracy calculated in step S6), the deep learning model is evaluated.
Optionally, in step S1), the remote sensing device is a satellite, a manned machine, an unmanned aerial vehicle or a hot air balloon; the different conditions comprise different illumination conditions, meteorological conditions, navigation height conditions, shooting angles, landform backgrounds and weir dam forming reasons; there is no duplication or intersection between the training image dataset and the test image dataset.
Optionally, the preprocessing in step S2) includes:
enhancing the image to improve the visual effect of the image;
data amplification is carried out to increase the number of samples of the image;
the size specification is carried out to unify the size of the image;
the format is standardized, and the format of the image is unified.
Optionally, the image enhancement adopts one or more of smoothing, sharpening, contrast enhancement, brightness enhancement, saturation enhancement and color conversion processing; the data amplification adopts one or more of rotation, translation, shearing, zooming and overturning; the size specification takes 1024 pixels by 1024 pixels or 512 pixels by 512 pixels; the format specification adopts one of JPG, PNG, GIF and TIF.
Optionally, the selected label in step S3) classifies the particulate matter on the surface of the weir dam into one category, or designs multiple categories according to different conditions in step S1), or performs the categories according to certain pixel criteria.
Optionally, in step S4), the deep learning algorithm is one of two stages of fast R-CNN, Mask R-CNN, Cascade R-CNN and TridentNet algorithms or one of one stage of YOLO, SSD, DSSD, RetinaNet, CornerNet and CenterNet algorithms.
Optionally, the identification precision in step S7) includes two indexes, i.e., an accuracy P and a recall R, and the calculation formulas are respectively:
Figure BDA0003050209700000031
Figure BDA0003050209700000032
wherein TP is the number of correctly detected surface layer particulate matters of the weir dam, TPFP is the number of all detected surface layer particulate matters of the weir dam, and TPFN is the number of surface layer particulate matters of the weir dam which should be detected;
the particle size measurement and calculation precision comprises an error x of the particle sizeiAverage value of
Figure BDA0003050209700000033
And standard deviation sigma, the calculation formula is respectively as follows:
xi＝Lm-Lt，
Figure BDA0003050209700000034
Figure BDA0003050209700000035
wherein L ismFor measuring and calculating the particle size, L, of a certain object to be identifiedtThe number n is the number of the targets to be identified.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method for detecting the surface granular substances of the weir dam based on deep learning provided by the invention designs a complete set of complete technical process and scheme, can realize identification, positioning and particle size measurement and calculation of the surface granular substances of the weir dam under complex environmental conditions, has the characteristics of simple process, reliable calculation, high identification speed, high accuracy and strong robustness, reduces the workload of a manual detection mode commonly adopted in the past, and provides good data basis and decision basis for structural analysis and emergency rescue and disaster relief of the subsequent weir dam.
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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 embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
Fig. 1 is a flowchart of a method for detecting particulate matter on the surface of a weir dam based on deep learning according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method for detecting the surface granular substance of the weir dam based on deep learning, which is used for realizing the automatic detection of the surface granular substance of the weir dam and has the characteristics of simple process, reliable calculation, high identification speed, high accuracy and strong robustness.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Fig. 1 is a flowchart of a method for detecting particulate matter on a surface layer of a weir dam based on deep learning according to an embodiment of the present invention, and as shown in fig. 1, the method for detecting particulate matter on a surface layer of a weir dam based on deep learning according to an embodiment of the present invention includes the following steps:
s1) carrying a digital camera by using a remote sensing device, acquiring color images of the surface granular substances of the weir dam under different conditions under the condition of natural illumination, and establishing a training image data set and a test image data set; the remote sensing device adopts a satellite, a man-machine, an unmanned aerial vehicle or a hot air balloon, and can also directly adopt a hand-held camera mode; when an image is collected, image samples under different conditions are selected as much as possible to enrich the types of the samples and improve the quality of model training, wherein the different conditions include but are not limited to different illumination conditions (morning, noon, evening and the like), meteorological conditions (clear, cloudy, foggy and the like), navigation conditions (set according to the precision of a detection target and the ground size GSD represented by one pixel), shooting angles, landform backgrounds and weir dam forming reasons (types of mountain landslides, earthquakes, lava and the like); no repetition or intersection exists between the training image data set and the testing image data set;
s2) preprocessing the training image data set to form a standard training image data set so as to enhance identifiability of related information, weaken unrelated information in the image and improve adaptability of the model to data; the pretreatment comprises the following steps:
image enhancement to improve the visual effect of images, usually applied to the case of poor visibility conditions; the image enhancement adopts but is not limited to smoothing (neighborhood averaging, median filtering, multi-image averaging, and the like), sharpening, contrast enhancement, brightness enhancement, saturation enhancement, and color transformation;
data amplification is carried out to increase the number of samples of the image, effectively relieve the overfitting condition of the model and bring stronger generalization capability to the model; the data amplification adopts but is not limited to rotation, translation, shearing, zooming and overturning;
the size specification is carried out to unify the size of the image; the size specification employs, but is not limited to, 1024 pixels by 1024 pixels or 512 pixels by 512 pixels;
the format is standard, and the formats of the images are unified; the format specification adopts but is not limited to JPG, PNG, GIF, TIF;
s3) manually labeling the surface layer particulate matter of the weir dam in the standard training image data set according to the selected label to generate a labeled file set; annotation software such as, but not limited to, LabelImg, LabelMe, annotation form such as, but not limited to, rectangle, polygon, circle; the selected label classifies the surface granular substances of the weir dam into one class, or designs multi-classification according to different conditions in the step S1), or can classify according to certain pixel standard; for example, two types of particulate matter are defined for the surface of a weir dam: a large target, and a small target, where a large target is defined as a particle size pixel of the particulate matter greater than α, and a small target is defined as a particle size pixel of α or less, and the parameter α may be an absolute value (such as, but not limited to, 32 pixels) or a relative value (such as, but not limited to, 0.1 of the width or height of the image size); when labeling, a certain number of negative samples are generally set, and the types of the negative samples are such as, but not limited to, water bodies, roads and vegetation; after each training image is labeled, a labeling file (such as but not limited to XML and JSON formats) is generated, and the area coordinates of each label type in the image are recorded;
s4) training the standard training image data set obtained in the step S2) and the labeled file set obtained in the step S3) based on a deep learning algorithm, and generating a deep learning model after training is completed; the deep learning algorithm is one of two-stage algorithms of fast R-CNN, Mask R-CNN, Cascade R-CNN, TridentNet and the like or one of one-stage algorithms of YOLO, SSD, DSSD, RetinaNet, CornerNet, CenterNet and the like, and the target detection of the surface granular substances of the weir dam by utilizing the algorithms and the improvement thereof is within the protection range of the invention and is not repeated herein;
in the embodiment of the invention, the SSD algorithm is taken as an example to explain the improvement condition of the deep learning algorithm; the SSD algorithm uses a multi-scale feature layer for detection by using the idea of a YOLO grid and an anchor mechanism of FasterR-CNN for reference, so that the SSD can quickly predict and relatively accurately acquire the position of a target; the anchor arrays of the feature layers are increased from small to large so as to detect targets with different scales; the front characteristic layer is used for detecting a small target, and the rear characteristic layer is used for detecting a large target;
the anchors can be set in a proportional and uniform distribution manner, taking the width of the training image as an example, assuming that the width is W (unit: pixel), the minimum anchor is set as W0.1, the maximum anchor is set as W0.95, and the intermediate anchors are uniformly distributed between W0.1-W0.95; the anchor array can also be set by statistical analysis. Selecting a certain number of images, recording the pixel size of the granular substances on the surface layer of the weir dam to form a set M, taking the width of a rectangular frame as an example, and then calculating an anchor array suitable for the granular substances on the surface layer of the weir dam by adopting a clustering algorithm in the field of data mining; here, the K-means algorithm is taken as an example, and the steps are as follows:
(1) setting the number of anchor arrays as N, namely the number of clusters;
(2) sorting the set M from small to large, wherein the minimum value is MminMaximum value of MmaxAt MminAnd MmaxRandomly selecting N values as a clustering center, thus forming N clusters;
(3) for each sample value s in the set M, respectively calculating the distance from the sample value s to N clustering centers, and dividing s into clusters where the clustering centers closest to the sample value s are located;
(4) recalculating the center of each cluster, and adopting an average value method;
(5) returning to the step (2) for iterative operation until the clustering center of each cluster tends to converge;
after the iterative operation, an anchor array which is relatively suitable for the surface granular substance of the weir dam can be obtained, and the quality and the migration capability of the training model can be improved;
s5) carrying out target identification on the surface particulate matter of the weir dam in the test image data set obtained in the step S1) by using a deep learning model, and calculating identification precision; performing frame selection marking on the target according to the set reliability, wherein the frame selection area is the detected surface layer particulate matter of the weir dam, and recording the pixel coordinates of the detected surface layer particulate matter;
s6) carrying out particle size measurement and calculation on the identification target based on a three-dimensional reconstruction algorithm, and calculating the particle size measurement and calculation precision; on the basis of the known image position and attitude parameters of the unmanned aerial vehicle (or other remote sensing devices), the three-dimensional reconstruction algorithm in computer vision or digital photogrammetry is utilized, the target identification result (namely the frame-selected pixel coordinates) in the step S5) is used as input, the solid dimension of the identification target is calculated, and the particle size information (such as width and height) of the surface layer particulate matter of the weir dam can be obtained; wherein, the three-dimensional reconstruction algorithm is such as but not limited to a front intersection algorithm, a bundle adjustment, SFM;
s7) evaluating the deep learning model by adopting two indexes of the identification precision calculated in the step S5) and the particle size measuring and calculating precision calculated in the step S6); the identification precision comprises two indexes of accuracy rate P (precision) and recall rate R (recall), and the calculation formulas are respectively as follows:
Figure BDA0003050209700000071
Figure BDA0003050209700000072
wherein TP is the number of correctly detected surface layer particulate matters of the weir dam, TPFP is the number of all detected surface layer particulate matters of the weir dam, and TPFN is the number of surface layer particulate matters of the weir dam which should be detected; the value range of the accuracy P is 0-1, the false alarm condition in the detection result is reflected, and the higher the value is, the higher the accuracy is, the less the false alarm is; the recall ratio R ranges from 0 to 1, the report missing condition in the detection result is reflected, and the higher the value is, the less the report missing is;
the particle size measurement and calculation precision comprises an error x of the particle sizeiAverage value of
Figure BDA0003050209700000073
And standard deviation sigma, the calculation formula is respectively as follows:
xi＝Lm-Lt，
Figure BDA0003050209700000074
Figure BDA0003050209700000075
wherein L ismFor measuring and calculating the particle size, L, of a certain object to be identifiedtThe number n is the number of the targets to be identified; the average x reflects the average level of the particle size error, and the labeling difference σ reflects the dispersion degree of the particle size error.
The embodiment of the invention provides a method for detecting surface particulate matter of a weir dam based on deep learning, which comprises the steps of selecting 1500 training images with the size of 1024 x 1024 pixels, marking 10 detection targets on each image on average, selecting 6 test images for experiment, and obtaining a detection result: the image recognition accuracy of the model reaches 95.67%, the recall rate reaches 89%, and the recognition precision reaches 2 cm.
The method for detecting the surface granular substances of the weir dam based on deep learning provided by the invention designs a complete set of complete technical process and scheme, can realize identification, positioning and particle size measurement and calculation of the surface granular substances of the weir dam under complex environmental conditions, has the characteristics of simple process, reliable calculation, high identification speed, high accuracy and strong robustness, reduces the workload of a manual detection mode commonly adopted in the past, and provides good data basis and decision basis for structural analysis and emergency rescue and disaster relief of the subsequent weir dam.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.

Claims (7)

1. A method for detecting surface particulate matter of a weir dam based on deep learning is characterized by comprising the following steps:
s1) a remote sensing device is used for carrying a digital camera to obtain color images of the surface layer particulate matter of the weir dam under different conditions, and a training image data set and a test image data set are established;
s2) preprocessing the training image data set to form a standard training image data set;
s3) manually labeling the surface layer particulate matter of the weir dam in the standard training image data set according to the selected label to generate a labeled file set;
s4) training the standard training image data set obtained in the step S2) and the labeled file set obtained in the step S3) based on a deep learning algorithm, and generating a deep learning model after training is completed;
s5) carrying out target identification on the surface particulate matter of the weir dam in the test image data set obtained in the step S1) by using a deep learning model, and calculating identification precision;
s6) carrying out particle size measurement and calculation on the identification target based on a three-dimensional reconstruction algorithm, and calculating the particle size measurement and calculation precision;
s7) using the two indices of the recognition accuracy calculated in step S5) and the particle diameter measurement accuracy calculated in step S6), the deep learning model is evaluated.
2. The method for detecting the granular substances on the surface layer of the weir dam based on the deep learning of claim 1, wherein in the step S1), the remote sensing device is a satellite, a manned machine, an unmanned aerial vehicle or a hot air balloon; the different conditions comprise different illumination conditions, meteorological conditions, navigation height conditions, shooting angles, landform backgrounds and weir dam forming reasons; there is no duplication or intersection between the training image dataset and the test image dataset.
3. The method for detecting particulate matter on the surface of a weir dam based on deep learning of claim 1, wherein the preprocessing in step S2) comprises:
enhancing the image to improve the visual effect of the image;
data amplification is carried out to increase the number of samples of the image;
the size specification is carried out to unify the size of the image;
the format is standardized, and the format of the image is unified.
4. The method for detecting the particulate matter on the surface layer of the weir dam based on the deep learning of claim 3, wherein the image enhancement adopts one or more of smoothing, sharpening, contrast enhancement, brightness enhancement, saturation enhancement and color conversion processing; the data amplification adopts one or more of rotation, translation, shearing, zooming and overturning; the size specification takes 1024 pixels by 1024 pixels or 512 pixels by 512 pixels; the format specification adopts one of JPG, PNG, GIF and TIF.
5. The method for detecting particulate matter on the surface of a weir dam based on deep learning of claim 1, wherein the selected label in step S3) classifies the particulate matter on the surface of the weir dam into one class, or designs multiple classes according to different situations in step S1), or classifies the particulate matter according to certain pixel criteria.
6. The method for detecting particulate matter on the surface of a weir dam based on deep learning of claim 1, wherein the deep learning algorithm in step S4) is one of two-stage fast R-CNN, Mask R-CNN, Cascade R-CNN and TridentNet algorithms or one of one-stage YOLO, SSD, DSSD, RetinaNet, CornerNet and cenerten algorithms.
7. The method for detecting particulate matter on the surface of a weir dam based on deep learning of claim 1, wherein the identification precision in step S7) includes two indexes of accuracy P and recall R, and the calculation formulas are respectively:
Figure FDA0003050209690000021
Figure FDA0003050209690000022
wherein TP is the number of correctly detected surface layer particulate matters of the weir dam, TPFP is the number of all detected surface layer particulate matters of the weir dam, and TPFN is the number of surface layer particulate matters of the weir dam which should be detected;
the particle size measurement and calculation precision comprises an error x of the particle sizeiAverage value of
Figure FDA0003050209690000025
And standard deviation sigma, the calculation formula is respectively as follows:
xi＝Lm-Lt，
Figure FDA0003050209690000023
Figure FDA0003050209690000024
wherein L ismFor measuring and calculating the particle size, L, of a certain object to be identifiedtThe number n is the number of the targets to be identified.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114331160A (en) * 2021-12-30 2022-04-12 四川大学 Damming dam burst disaster chain mode identification method based on landslide and river plugging form
CN115346114A (en) * 2022-07-21 2022-11-15 中铁二院工程集团有限责任公司 Method and equipment for identifying and positioning bad geologic body by railway tunnel aviation electromagnetic method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109801282A (en) * 2019-01-24 2019-05-24 湖北大学 Pavement behavior detection method, processing method, apparatus and system
CN110047072A (en) * 2019-04-30 2019-07-23 福建南方路面机械有限公司 A kind of gravel size identification processing system and processing method based on mobile interchange
CN110070537A (en) * 2019-04-25 2019-07-30 清华大学 The granularity of still image particle and the intelligent identification Method of sphericity and device
CN110211173A (en) * 2019-04-03 2019-09-06 中国地质调查局发展研究中心 A kind of paleontological fossil positioning and recognition methods based on deep learning
CN110390691A (en) * 2019-06-12 2019-10-29 合肥合工安驰智能科技有限公司 A kind of ore scale measurement method and application system based on deep learning
CN111079847A (en) * 2019-12-20 2020-04-28 郑州大学 Remote sensing image automatic labeling method based on deep learning
CN111161292A (en) * 2019-11-21 2020-05-15 合肥合工安驰智能科技有限公司 Ore size measurement method and application system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109801282A (en) * 2019-01-24 2019-05-24 湖北大学 Pavement behavior detection method, processing method, apparatus and system
CN110211173A (en) * 2019-04-03 2019-09-06 中国地质调查局发展研究中心 A kind of paleontological fossil positioning and recognition methods based on deep learning
CN110070537A (en) * 2019-04-25 2019-07-30 清华大学 The granularity of still image particle and the intelligent identification Method of sphericity and device
CN110047072A (en) * 2019-04-30 2019-07-23 福建南方路面机械有限公司 A kind of gravel size identification processing system and processing method based on mobile interchange
CN110390691A (en) * 2019-06-12 2019-10-29 合肥合工安驰智能科技有限公司 A kind of ore scale measurement method and application system based on deep learning
CN111161292A (en) * 2019-11-21 2020-05-15 合肥合工安驰智能科技有限公司 Ore size measurement method and application system
CN111079847A (en) * 2019-12-20 2020-04-28 郑州大学 Remote sensing image automatic labeling method based on deep learning

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114331160A (en) * 2021-12-30 2022-04-12 四川大学 Damming dam burst disaster chain mode identification method based on landslide and river plugging form
CN114331160B (en) * 2021-12-30 2023-04-28 四川大学 Dam blocking and dam bursting disaster chain mode identification method based on landslide river blocking form
CN115346114A (en) * 2022-07-21 2022-11-15 中铁二院工程集团有限责任公司 Method and equipment for identifying and positioning bad geologic body by railway tunnel aviation electromagnetic method

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