teLEDETECCION CON LIDAR

1. UAV Dam Crack Detection System Based
on Beidou and LIDAR
Junjie Wang, Chengyao Tan, Tong Hu, Yu Xie, Yang Zhang, and Linlin Cui(B)
College of Resources and Environment, Chengdu University of Information Technology,
Chengdu 610225, China
cuilinlin@cuit.edu.cn
Abstract. With the rapid development of dam construction, the demand for dam
management and maintenance is rapidly expanding. Dam cracks are the core of
dam management and maintenance, and rapid and effective detection of cracks
and their spatial location and geometric parameters are the basis for dam main-
tenance. However, dam safety supervision mainly relies on manual work, which
is very labor-intensive and time-consuming, and the detection is not fine enough.
Therefore, we propose a new and efficient low-cost dam inspection system - UAV
dam crack detection system based on Beidou and LIDAR. The UAV is equipped
with LIDAR to obtain a high-precision three-dimensional point cloud of the dam
surface, use the filtering algorithm to obtain the height difference and intensity
contrast of the same dam surface, and then obtain the candidate point cloud of
cracks according to the height difference and intensity contrast, use the maximum
entropy threshold segmentation method to extract the candidate point cloud, and
then filter and denoise the candidate point cloud based on the trough effect, and
extract the cracks and their geometric parameters according to the morphological
filtering. The texture information obtained from the conventional optical camera
is used to construct a 3D model of the dam, and the cracks are displayed on the
3D model to statistically evaluate the severity of the cracks. The system aims
to get more and more accurate information about the location and geometry of
dam cracks through the combination of Beidou system and LIDAR, so as to better
detect the hidden danger of dam safety, save human and material resources for dam
safety management, and make the management more convenient and efficient.
Keywords: Beidou · UAV · LIDAR · Crack detection
1 Introduction
In recent years, with the rapid development of China’s economy and the need for infras-
tructure construction, water conservancy projects have been developed on a large scale.
However, with the growth of dam operation time, the dam body due to water erosion and
structural aging and other factors and cracks, and eventually lead to dam safety risks,
and cracks will gradually extend to the inside of the dam body, from the quantitative
change caused by qualitative change, and eventually lead to breakage. In addition, dam
leakage may lead to salinization, marshification and other phenomena, which will cause
huge economic losses, and even serious casualties.
© Aerospace Information Research Institute 2024
C. Yang and J. Xie (Eds.): CSNC 2024, LNEE 1092, pp. 3–13, 2024.
https://doi.org/10.1007/978-981-99-6928-9_1

2. 4 J. Wang et al.
Inthecrackfieldsurveyof68damsectionsofTaipingwanDamin2019,8crackswere
found in only 2 dam sections [1]. More than 50 cracks were found in a crack inspection
of Zhu Zhuang Reservoir in 2020 [2], and a large number of cracks of different sizes
were also found in a crack inspection of the Zhuchang River Reservoir in 2021 [3]. The
statistics released by the Chinese Society of Dam Engineering at the end of 2020 show
that dam crack detection is extremely important, but, relying on manual inspection, the
workload is large, the work cycle is long, which can waste huge energy as well as financial
resources, and may be dangerous [4]. In addition, due to the staff being suspended high
in the air, the observation field of view is limited, which may also produce problems
such as incomplete and meticulous exclusion. Therefore, there is an urgent need to carry
out intelligent detection of cracks in dams in China.
This paper presents a technology scheme of UAV combined with Beidou positioning
and LIDAR to achieve intelligent detection of cracks in dams, solving all the inconve-
niences of manual ranking, improving work efficiency and ensuring staff safety. At the
same time, the cracks can be displayed in three dimensions to improve the maintain-
ability of the dam, which has practical application and economic value and is of great
significance for the scientific prevention and control of dam disasters.
2 Current Status of Research
Reservoir dams are the key targets of low-altitude protection in China, and are the
key research projects in water conservancy engineering. In order to prevent dangerous
accidents and guarantee the safe use of dams for a long time, it is very important to study
fast and efficient dam crack detection technology to guarantee the safety and stability of
dams and reservoirs.
The traditional crack monitoring method is the manual detection method, which finds
theareawherecracksmayexistbymanualvisualinspection,andthenmanuallymeasures
its length, width and other main characteristics. Manual contact with the surface of the
dam detection is very prone to safety accidents, there are large risks and large workload;
subjective, low reliability, the results are not intuitive, poor standardization; due to the
impact of errors, accuracy and other issues, the results only have reference value, can
not establish the full cycle of the dam crack operation system, poor expandability.
In order to improve the efficiency, accuracy and safety of detection, many scholars
have conducted research in recent years. On the one hand, Ouyang proposed to establish
an intelligent dam distributed fiber-optic intelligent sensing system for prediction and
detection of cracks in dams based on the advantages of high sensitivity and easy installa-
tion of fiber-optic sensors [5]. With the distributed fiber optic sensor to capture random
cracks and use the distributed fiber optic strain detection system to forecast and detect
cracks, this system can achieve real-time detection and feedback of cracks in the dam to
ensure the long-term safety and stability of the dam. On the other hand, due to the wide
application of nondestructive testing method in engineering inspection, Zhang proposed
a geological radar detection technology for dam crack detection [6], by emitting high-
frequency electromagnetic pulses to detect the location and geometric parameters of the
dam cracks, so as to carry out rapid and accurate detection of the large and complex
structure of the dam. In addition, due to the rapid development of computer technology,

3. UAV Dam Crack Detection System Based on Beidou and LIDAR 5
machine vision detection of cracks has also become one of the widely used technologies
nowadays. Among them, Xu et al. proposes to detect the leakage of dam surface based
on UAV platform combined with tilt photography technology, which collects data by
UAV and constructs a model by tilt photography to detect cracks, surface breakage and
leakage of dams [7]. Deng et al. further proposes to use UAV tilt photography measure-
ment technology combined with machine vision crack recognition to detect cracks on
the surface of the dam, and to rank the cracks existing in the dam through UAV data col-
lection, tilt photography 3D modeling and improved full convolutional neural network
(I-CFN) [8].
The above-mentioned methods are more convenient, flexible and targeted than tradi-
tional manual inspection, which to a certain extent improves the efficiency of dam crack
detection and reduces the risk of personnel and equipment safety, and is a new means of
dam safety inspection. However, the quality of dam crack images is affected by the envi-
ronmentandoftensuffersfromlowcontrast,unevenillumination,andblurredunderwater
dam images, and the effect of crack extraction using digital image processing techniques
is very unsatisfactory, with problems such as texture noise and incomplete extraction
of complex cracks. Based on the above problems, this project proposes an unmanned
aerial vehicle (UAV) dam crack detection system based on Beidou and LIDAR, using
UAVs equipped with various sensors such as LIDAR, combined with Beidou navigation
and positioning system for crack detection and its location and attribute information
extraction. The UAV operation is efficient and convenient, and LIDAR can accurately
obtain the geometric features of cracks and other features such as surface deformation
and structural damage of the dam. Based on this system, it can better detect the hidden
danger of dam safety, save manpower and material resources for dam safety manage-
ment, and make the management more convenient and efficient. In the future, a crack
database can be established in combination with artificial intelligence, big data analysis,
Internet of Things and other technologies to establish a whole life cycle operation system
for dam inspection and maintenance.
3 System Introduction
The system is mainly divided into five parts: data acquisition, data management, data
processing, accuracy verification, and control center (Fig. 1). Data acquisition part:
Based on the DJI M300 UAV with Beidou system, it carries optical camera and LIDAR
to photograph and scan the surface of the dam to get the laser point cloud data and
image data of the dam surface. Data management part: find the historical information
of the dam and store it and the obtained point cloud and image in the database for
easy organization and management. Data processing part: Use the laser point cloud
data to detect cracks in the dam, and count the geometric parameters of the cracks and
classify and grade the cracks. Accuracy verification part: The preliminary crack results
are synchronized to the maintenance personnel, and the specific images of cracks are
uploaded through handheld terminal devices to compare with the algorithm results for
accuracy verification. Control center part: firstly, the obtained cracks are presented in the
3D model of the dam, and a crack distribution map is displayed to evaluate and classify
the severity of cracks; secondly, as the interaction place of the three processes of data

4. 6 J. Wang et al.
acquisition, data processing and accuracy verification, the function of data transmission
is executed to guarantee the synchronization of information between decision makers
and implementers.
Fig. 1. System composition
4 System Implementation
4.1 Data Acquisition
The data acquisition system uses UAVs equipped with LiDAR, Beidou chips, inertial
measurement units (IMUs), and optical cameras to conduct aerial flights of the dam
to obtain information about its location and attributes. The optical camera collects the
dam texture information, and the Beidou satellite navigation and positioning system
(BDS) [9] and LiDAR provide real-time scanning data for secondary decoding of the
coordinates of the 3D point cloud.
The UAV provides full coverage of the dam through route setting; LIDAR detects
the distance and angle between it and the target dam; Beidou satellite navigation and
positioning system (BDS) to obtain the position information of the UAV in real time;
Inertial Measurement Unit (IMU) outputs the instantaneous attitude information of the
sensor such as angle elements including heading angle, roll angle and pitch angle in
real time; optical camera collects the texture information of the dam for subsequent 3D
display. Figure 2 shows the data and relative position relationships collected by the three
main sensors: LiDAR, Beidou satellite navigation and positioning system (BDS), and
inertial measurement unit (IMU), and serves to solve the coordinates of the 3D point
cloud.
The meanings of the parameters in Fig. 2 are as follows: (XP, YP, ZP) represents the
3D coordinates of the target point of the dam; (XBDS, YBDS, ZBDS) is the BDS antenna

5. UAV Dam Crack Detection System Based on Beidou and LIDAR 7
Fig. 2. Schematic diagram of the calculation principle of 3D point cloud coordinates [10]
center coordinates; (ω, ϕ, κ), RM
IMU (ω, ϕ, κ) represent the sensor transverse rocking,
pitch and yaw angles provided by the IMU and the rotation matrix of the IMU to the
measurement coordinate system, respectively. rP
S(αd) is the coordinate vector of the
target point P with respect to the scanner coordinate system, α and d denote the scanning
angle and measurement distance, respectively; RIMU
S (ω, ϕ, κ) represents the
rotation matrix from the laser scanner coordinate system to the IMU coordinate system,
and (ω, ϕ, κ) is the deflection angle between the scanner and the IMU coordinate
system, respectively, which is determined by the system check calibration; (lX, lY, lZ),
(LX, LY, LZ) represent the rotation matrix from the IMU origin to the LIDAR origin and
the BDS origin, respectively. Offset from IMU origin to LIDAR origin and BDS origin,
respectively.
Based on the above data, the relationship between the coordinates of each target
point in the 3D point cloud of the dam is solved by the following Eq. (1):
⎡
⎢
⎣
XP
YP
ZP
⎤
⎥
⎦
M
=
⎡
⎢
⎣
XBDS
YBDS
ZBDS
⎤
⎥
⎦
M
+ R N
MUU (ω,ϕ,κ)
.
⎛
⎜
⎜
⎝ RINU
S (ω, ϕ, κ) · rs
ρ(ad) +
⎡
⎢
⎣
lX
lY
lZ
⎤
⎥
⎦
IMU
S
−
⎡
⎢
⎣
LX
LY
Lz
⎤
⎥
⎦
IMU
GNSS
⎞
⎟
⎟
⎠ (1)
Based on this, the high-precision 3D point cloud acquisition and analysis of the dam
surface can be realized, and the real-time monitoring of the UAV flight path and the
real-time acquisition and analysis of the location of the acquisition point can also be
realized. If abnormalities are detected in the flight path of the UAV or in the location of
the acquisition point, the data acquisition device can provide timely feedback so that the
data acquisition plan can be adjusted in a timely manner.

6. 8 J. Wang et al.
4.2 Data Management
The dam data management part is three modules: 3D point cloud data management
module, optical image data management module, and historical crack detection data
management module. The 3D point cloud data management module mainly stores the
data acquired by three sensors, namely LiDAR, BDS and IMU, as well as the later
3D point cloud coordinate solution results. The optical image data management mod-
ule mainly stores the texture data of the dam captured by the optical camera and the
subsequent processing results, including real-time image data, pre-processed data and
historical image data. The historical crack detection data management module mainly
includes the aggregation of historical crack detection results to form the ROI area (crack
risk area) of the dam.
4.3 Data Processing
Dam crack detection requires not only detecting the presence of cracks and distinguish-
ing the types of cracks, but also counting the physical characteristics of the cracks,
such as their location, width, length, area, and orientation. Among them, crack width is
an important basis for classifying crack types [11]. There are many methods for crack
extraction, such as threshold segmentation, finite element method and Fully Convolu-
tional Networks. This paper focuses on the recognition method based on laser point
cloud data.
After a crack appears in the dam, the height value and laser reflection intensity
value at the crack are lower than those at the surface of the dam. Therefore, the height
difference and intensity contrast between the crack and the surface of the dam body are
first obtained using the filtering algorithm, and then the candidate point cloud of the
crack is obtained based on the height difference and intensity contrast. When detecting
the candidate point cloud, the candidate point cloud is extracted using the maximum
entropy threshold segmentation method, and then the candidate point cloud is filtered and
denoised based on the trough effect. Finally, the cracks and their geometric parameters
are extracted based on morphological filtering [10, 12].
Height difference and strength contrast relative to the surface of the dam.
With any point as the center, the median of its eight neighborhoods is used as the intensity
value of the local dam, and the point cloud in the height difference neighborhood is least-
squares fitted to obtain the local dam surface. The height difference and intensity contrast
between the center point cloud and the local dam surface are calculated and stored in
PH and PI, respectively, as shown in the following Eq. (2).
PH = Pm − Pz; PI = Im − Iz (2)
Pm and Im are the m-th point cloud, respectively, and IZ and PZ are the local dam
strength and elevation values obtained by median filtering and least squares fitting,
respectively.

7. UAV Dam Crack Detection System Based on Beidou and LIDAR 9
Extraction of Crack Candidate Points.
Select point clouds that meet the height difference condition and slope condition as low
valley candidates in PH:
h(i, j) −hd
slopef (i, j) vh
(3)
hd denotes the maximum depth the crack may reach, slopef (i,j) denotes the average
slope centered at (i, j), calculated by 8-neighborhood coordinates, and vh denotes the
minimum gradient variation requirement of the candidate point cloud, which needs to
be set by itself. The extracted trough candidate point cloud is stored in the dataset Gy,
see above Eq. (3).
slopef (i, j) =
1
k
k
1
(slopet(i, j, m))
;
slopet(i, j, m) =
Z(i, j, k) − Z(i, j)
(X (i, j, k) − X (i, j))2 + (Y(i, j, k) − Y(i, j))2
(4)
k denotes the number of valid point clouds in the 8-neighborhood centered at (i, j),
and slopet(i,j) is the slope of the m-th point cloud to the center point. x(i,j,k), y(i,j,k),
and z(i,j,k) denote the 3D coordinates of the k-th point cloud, and x(i,j), y(i,j), and z(i,j)
denote the 3D coordinates of the center point cloud, respectively, see the above Eq. (4).
The point cloud with lower reflection intensity than the dam surface is extracted
accordingtothemaximumentropythresholdsegmentationmethodinPI, andamaximum
intensity difference value F is set for the crack point cloud. This value can be selected
according to the histogram to account for 5% of the corresponding intensity value. The
point clouds that satisfy the intensity difference are selected and stored in Gc. The same
point clouds may be found in Gc and Gv, and the concatenated set of the two data sets
is taken as G.
Morphological Filtering and Accuracy Verification.
Morphological filtering is performed for the obtained candidate point clouds, using
closed-operator processing with expansion followed by erosion. Eight-neighborhood is
used for joint zone selection, and the minimum number of point clouds contained in the
joint zone is set as C. The independent patches with the number of point clouds less than
C are deleted. Extract the shape parameters of the joint zone: maximum length (along
the x-axis direction) and maximum width (along the y-axis direction). The larger value
of the crack length and width is compared with the set crack length threshold L. If it is
larger than the threshold L, this area is a crack.
To ensure the accuracy of crack detection, manual field visits are used for verifi-
cation, and handheld terminals are equipped for technicians. Based on the laser point
cloud detection results, the decision maker indexes the spatial location information of
the crack in the data management system and transmits its spatial location information
and geometric parameters to the operator’s handheld terminal. According to the infor-
mation obtained, the handheld terminal provides the operator with precise navigation

8. 10 J. Wang et al.
positioning of the area, which facilitates the operator to find the crack quickly and accu-
rately. After the operator takes the actual measurement of the detection result area, the
actual measurement results and images are transmitted to the data management system
through the terminal. The decision maker verifies the accuracy of the laser point cloud
extraction results with the field inspection results, and on this basis guarantees the credi-
bility and validity of the algorithm results. The crack detection results after the accuracy
verification are fed back to the technicians as the final detection results to execute rel-
evant remedial measures, and the location information of each crack is matched with
the relevant repair records for perfection to facilitate the construction of a perfect crack
database at a later stage.
4.4 Control Center
The control center includes two main parts: data transmission and data display. Data
transmission includes: transferring the results of 3D point cloud coordinate solution and
preliminary crack detection results to the data management system; transferring the crack
detection results to the operator’s handheld terminal as well as receiving the operator’s
fieldwork results; releasing the manager’s decision scheme and receiving the operator’s
actual feedback to realize the efficiency of the crack detection process and improve
the reliability of the crack detection results, data transmission process. The data display
mainly completes the visualization of crack detection results, point cloud 3D models, and
the fitting results of tilt photography images and point cloud 3D models, such as crack
distribution maps, zoning and grading detection statistics, and crack detection result
analysis. Based on such dam information, the decision management personnel realize
digital management of dam cracks, which makes dam crack detection more convenient
and provides data basis for dam crack remediation plan with more scientificity.
5 Crack Detection Simulation Experiment
Because of the expensive equipment required and the difficulty in obtaining rel-evant
data, this study uses simulated data (DEM images based on laser point cloud generation),
to verify the feasibility of this scheme and to demonstrate the crack detection results, as
well as to evaluate the accuracy of the results.
The simulation experiment uses DEM images based on laser point cloud generation
and field images to simulate real dam cracks. First, based on the DEM image, the crack
detection was performed by using the threshold segmentation method; second, based on
the field image, the real crack results were obtained by visual interpretation; finally, the
real cracks were compared and analyzed with the experimental results to verify the crack
detection accuracy. It is verified that the crack detection accuracy of this experiment is
82%, which can detect large cracks, but fails to detect fine cracks. The experimental
results are shown in Fig. 3.

9. UAV Dam Crack Detection System Based on Beidou and LIDAR 11
Fig. 3. Crack detection results of simulation experiment (Note: Figure (a) is DEM image gener-
ated based on laser point cloud; Figure (b) is the crack detection results based on the threshold
segmentation method. Figure (c) is the result of visual interpretation of the fracture. Figure (d) is
the superposition result of crack detection based on threshold segmentation method and original
image)
6 Innovation and Application Prospects
6.1 Innovation Points
With the precise positioning function of Beidou positioning and navigation system,
combined with a variety of sensors such as LiDAR, we obtain high-precision dam crack
location and attribute data, and then use the dam crack detection algorithm to realize the
accurate detection of dam cracks.
Build a three-dimensional model of the dam for digital management, not only to
facilitate intuitive display and efficient management, but also the future combined with
artificial intelligence, Internet of Things and other technologies to establish the dam
crack monitoring, detection, maintenance of the whole life cycle of the operation system
to provide a solid data base.
6.2 Application Prospects
Can to a certain extent to solve the current dam crack detection is not accurate enough,
high cost, long detection time, can effectively reduce the cost of dam maintenance,
reduce the national maintenance of water conservancy projects on part of the pressure.
With the gradual application of a new generation of LIDAR scanning technology
in engineering inspection projects, the use of UAVs combined with LIDAR to detect
cracks in dams can be used to the maximum extent to achieve the application value of

10. 12 J. Wang et al.
the emerging technology, and can be extended to the safety inspection of aircraft take-off
runways, ship hulls, etc.
With the continuous improvement of Beidou positioning accuracy and LIDAR scan-
ning accuracy, the detection accuracy of dam cracks will also be improved, and in the
future, it will be organically combined with more types of sensors to make its use more
valuable.
6.3 Uncertainties and Shortcomings
Due to the lack of actual measurement data of UAV-mounted LIDAR detection of dam
cracks, the above study is limited to reasonable estimation within the theoretical range,
i.e., it adopts the current theory of relevant algorithms as the basis. Although simulation
experiments have been conducted, it is still necessary to verify the accuracy through
actual data to support the above theory.
The current program mainly focuses on the part of the dam above the water surface
for crack detection, the lack of underwater dam crack detection, and the actual shooting
is affected by bad weather and the depth of the water after the bad weather, with certain
limitations.
Due to the limitations of the current Beidou positioning accuracy and LIDAR scan-
ning accuracy, the detection of dam cracks based on the existing laser 3D point cloud
algorithm may make some of the dam cracks can not be accurately identified and located,
which still needs to be continuously improved and enhanced in the future.
Acknowledgments. Funded by Sichuan Science and Technology Program (2023NSFSC0250).
Ministry of Education industry-university cooperative education project (202102245026). College
Students’ Innovative Entrepreneurial Training Plan Program (202210621045, S202210621118).
References
1. Wu, W.Y., Fang, G.Z., Wei, Y.: Analysis on causes and harmness of cracks on dam crest using
numerical simulation. Yangtze River 51(S2), 270–274 (2020)
2. Tian, X.W.: Concrete crack repair of Zhuzhuang reservoir dam. Water Sci. Eng. Technol. 2,
46–49 (2020)
3. Deng,L.N.:CausesanalysistreatmenttechnologyofconcretecracksinZhuchangheReservoir
Dam. Water Conserv. Constr. Manage. 41(7), 44–48 (2021)
4. China Dam Engineering Society: China Dam Engineering Society 2020 Annual
Report. http://www.chincold.org.cn/chincold/rootfiles/2023/01/17/1671019479025604-167
1019479089257.pdf. Last accessed 20 Sept 2022
5. Ouyang, B.Y.: Application research of distributed optical fiber sensing technology in
intelligent dam safety monitoring. Technol. Innov. Appl 6, 6–7 (2016)
6. Zhang, C.: Application of geological radar technology to detect cracks in a dam body. Shanxi
Hydrotechn. 1, 25–27+31 (2018)
7. Xu, C.Y., Yang, Y., Zhong, L.: Research on slope safety monitoring technology based on
UAV proximity photogrammetry. Henan Water Resour. South-to-North Water Divers. 50(7),
82–84 (2021)

11. UAV Dam Crack Detection System Based on Beidou and LIDAR 13
8. Deng, Y.X., Luo XJ, S, Li HL, T.: Research on crack detection of hydropower station dam
surface based on tilt photogrammetry technology of UAV. Technol. Innov. Appl. 5, 158–
161+166 (2021)
9. Zhu, E.H., Guo, H.R., Li, J.L.: Performance analysis of real-time PPP based on BDS-3
PPP-B2b service. J. Geodesy Geodyn. 42(6), 616–621 (2022)
10. Zhong, M.Q.: Research on methods of pavement condition survey using mobile laser scanning
data (Dissertation). Changan University, Xian (2020)
11. Guan, H., Li, J., Cao, S., Yu, Y.: Use of mobile LiDAR in road information inventory: a review.
Int. J. Image Data Fusion 7(3), 219–242 (2016)
12. Xiao, W.T.: Research on detection and management of surface cracks in tunnel lining based
on 3D laser scanning (Dissertation). Xiamen University, Xiamen (2019)