Transcript Data mining for pothole detection Pro gradu seminar 10.2.2011

Data mining for pothole detection
Pro gradu seminar
10.2.2011
Hannu Hautakangas
Jukka Nieminen
Contents
 Introduction
 Related work
 Data
 Data preprocessing
 Feature extraction
 Feature selection
 Support vector machine
 Results
 Problems
 References
Introduction
 Purpose of the research is to detect anomalies on road
surface
 Expansion joints
 Potholes
 Speed bumps
 Etc.
 Supervisors
 Tapani Ristaniemi
 Fengyu Cong
Related work
 Accelerometer based techniques
 Pothole Patrol
 Nericell
 Terrain classification
 Other techniques
 Image detection
 Laser profilometer
 Ground penetrating radar
Data
 Acceleration data
 Contains lateral, longitudinal and vertical axis
 GPS position and timestamp for each measurement
 Class label for each measurement
 Sampling rate is 38 Hz
 Data was collected using several different vehicles
Data preprocessing
 Several filters were produced and tested using different
passbands in the frequency range 0.5 – 6 Hz
 Data was windowed using sliding window
 Different sliding window functions were tested
 Chebyshev
 Hamming
 Taylor
 Etc.
 Normalization in the range [0,1]
Original and filtered Y-axis data
Feature extraction
 Several different features were extracted
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Mean
Peak-to-peak ratio
Root mean square
Standard deviation
Variance
Power spectrum density
 21 frecuency bins in the frequency range 1-5 Hz
 Partial sum of the frequency bins in the frequency range 1-5Hz
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First sum is between 1-2 Hz, second in 2-3 Hz, etc.
 Wavelet packet decomposition
 This was done by Fengyu Cong
Feature extraction
Feature selection
 Feature selection is used to reduce the number of features
and thus reduce the computational effort and make the
classification operation faster and more accurate
 Different techniques were tested
– Backward and forward selection
– Genetic algorithm
– Principal component analysis
Backward and forward selection
 Originally introduced by M. A. Efroymson 1960
 Tries to find best feature subset
 Model includes only significant features
 Features are usually evaluated using F-test
 Based on linear regression
 Feature is significant if it’s f-value > predetermined
significant level
Backward and forward selection
 Backward selection
 Starts with all features in the model
 Removes features one by one starting from most unsignificant
 Continues until model includes only significant features
 Forward selection
 Opposite to backward selection
 Starts with zero features in the model
 Adds features one by one starting from most significant
 Continues until all significant features are selected
Genetic algorithm
 Genetic algorithm is a computational model that searches a
potential solution to a specific problem using data structure,
which is inspired by evolution
 It was introduced by John Holland in 1975
 The algorithm can be considered as a two-stage process
 It begins with the current population where the best
chromosomes are selected, based on their fitness values, to
create an intermediate population
 Then crossover, mutation and reproduction is applied to create
the next population
 This two-stage process constitutes one generation
Genetic algorithm
 Crossover operation selects randomly two individuals ands
generates two new ones by combining the selected ones
 Mutation operation randomly selects an individual, removes
its subtree from randomly selected node and then generates a
new subtree
Genetic algorithm
 Reproduction operation moves selected individuals to next
population without any change
 Each feature is represented as a binary vector of dimension
m, where m is the amount of features
 Bit 1 means that the corresponding feature is part of the
subset and bit 0 means that the corresponding feature is not
part of the subset
Principal component analysis
 PCA was introduced by Karl Pearson in 1901
 The method was not able to calculate more than two or three
variables
 In 1933 Harold Hotelling described the methods for
computing multivariate PCA
Principal component analysis
 The object of PCA is to find uncorrelated principal
components Z1,Z2,…, Zp that describes the debendencies
between variables X1,X2,.., Xp
 Principal components are ordered so that the first
component Z1 displays the largest amount of variation in the
data, second component Z2 displays the second largest
amount of variation, and so on
 Principal components are selected based on their eigenvalues
Support vector machine
 Vladimir Vapnik introduced SVM in 1995
 A binary classification tool
 Tries to find optimal separating hyperplane to separate
classes from each other
 Basic SVM can classify only two classes but it can be
extended to multiclass classifier
Support vector machine
 Creates a model based on training data
 Each data sample has a class label
 Model predicts to which class a specific data sample belongs
 Model is tested using testing data
 Predicted labels are compared to known labels
 A Matlab library LIBSVM was used as an SVM-tool
 LIBSVM implements most of the common SVM methods
 Supports multiclass classification
Results
 Data was classified with SVM using PCA
 Two data sets
 Set 1 consists of 1779 normal and 12 anomaly samples
 Set 2 consists of 1779 normal and 21 anomaly samples
 Both sets have 30 features which are generated with wavelet
packet decomposition
 70% of the normal samples were used to create SVM model
 Rest of the normal samples (534) were used to test the model
 PCA was used to select the features that represents most of
the variation in the data
 Tests were run 1000 times to get proper results
 The results are mean values of 1000 test runs
Results
 Set 1
 With three or more principal components
 All 12 anomalies were classified correctly
 6.93 normal samples out of 534 were classified incorrectly
 Set 2
 With three principal components
 1.63 anomalies out of 21 were classified incorretly
 7.26 normal samples out of 534 were classified incorrectly
 With 10 or more principal components
 0.02 anomalies out of 21 were classified incorrectly
 6.53 normal samples out of 534 were classified incorrectly
Results, set 1
Results, set 2
Problems
 Timestamps are not accurate which affects the labeling of the
classes
 Some normal data samples are labeled as anomalies and vice
versa
 Small number of anomaly data samples compared to normal
data samples
 For example data set 1 has 12 anomaly and 1779 normal
samples
 Multiclass classification is difficult because there is not enough
anomaly samples to create multiclass SVM model
References
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Backward and forward selection
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N. R. Draper and H. Smith, Applied regression analysis 2nd edition, John Wiley & Sons Inc, 1981.
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M. A. Efroymson, Multiple regression analysis, in Mathematical Methods for Digital Computers, editors A. Ralston and H.S. Wilf, John
Wiley & Sons Inc,1960.
Genetic algorithm
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John Holland, Adaptation in natural and artificial systems : an introductory analysis with applications to biology, control, and artificial intelligence,
University of Michigan Press, 1975.
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L. B. Jack and Asoke K. Nandi, Genetic algorithms for feature selection in machine condition monitoring with vibration signals, in IEEE Signal
Processing Vol 147, No 3, June 2000.
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Darrell Whitley, A genetic algorithm tutorial, in Statistics and computing 4, pages 65-85, 1994.
PCA
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Harold Hotelling, Analysis of a complex of statistical variables into principal components, in Journal of Educational Psychology, volume 24, issue 7
pages 498-520, October 1933.
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Ian T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 2002.
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Karl Pearson, On lines and planes of closest fit to a system of points in space, Philosophical Magazine, Vol. 2, pages 559-572, 1901.
References
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Related Work
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W. Dargie, Analysis of time and frequency domain features of accelerometer measurements, Proceedings of 18th Internatonal Conference on
Computer Communications and Networks. ICCCN 2009, pages 1-6.
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DuPont, Edmond and Moore, Carl and Collins, Emmanuel and Coyle, Eric, Frequency response method for terrain classification
in autonomous ground vehicles, in Autonomous Robots, vol. 24, pages 337-347, 05/04, 2008.
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J. Eriksson, L. Girod, B. Hull, R. Newton, S. Madden and H. Balakrishnan, The pothole patrol: using a mobile sensor network for road surface
monitoring, in MobiSys 2008: Proceeding of the 6th international conference on Mobile systems, applications and services, ACM, New
York, 2008, pages 29-39.
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D.H. Kil, F.B. Shin, Automatic road-distress classification and identification using a combination of hierarchical classifiers and expert systems-subimage
and object processing, Proceedings of International Conference on Image Processing, pages 414 - 417 vol 2, Santa Barbara, CA, USA 1997.
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J. Lin and Y. Liu, Potholes detection based on SVM in the pavement distress image, in Ninth International Symposium on Distributed Computing
and Applications to Business, Engineering and Science, pages 544 - 547, Hong Kong, China 2010.
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P. Mohan, V. N. Padmanabhan and R. Ramjee, Nericell: Rich monitoring of road and traffic conditions using mobile smartphones, in SenSys 2008:
Proceedings of the 6th ACM conference on Embedded network sensor systems, ACM, New York, 2008, pagess 323-336.
SVM
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Corinna Cortes and Vladimir Vapnik, Support-Vector Networks, Machine Learning, Volume 20, pages 273-297, Kluwer Academic Publishers,
Boston, 1995.
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LIBSVM – A library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/, referred 4.2.2011.
Tack så mycket!