Data mining for pothole detectionPro gradu seminar10.2.2011

Hannu HautakangasJukka Nieminen

ContentsIntroductionRelated workDataData preprocessingFeature extractionFeature selectionSupport vector machineResultsProblemsReferences

IntroductionPurpose of the research is to detect

anomalies on road surfaceExpansion jointsPotholesSpeed bumpsEtc.

SupervisorsTapani RistaniemiFengyu Cong

Related workAccelerometer based techniques

Pothole PatrolNericellTerrain classification

Other techniquesImage detectionLaser profilometerGround penetrating radar

DataAcceleration data

Contains lateral, longitudinal and vertical axis

GPS position and timestamp for each measurement

Class label for each measurementSampling rate is 38 Hz

Data was collected using several different vehicles

Data preprocessingSeveral filters were produced and tested

using different passbands in the frequency range 0.5 – 6 Hz

Data was windowed using sliding windowDifferent sliding window functions were tested

ChebyshevHammingTaylorEtc.

Normalization in the range [0,1]

Original and filtered Y-axis data

Feature extractionSeveral different features were extracted

MeanPeak-to-peak ratioRoot mean squareStandard deviationVariancePower spectrum density

21 frecuency bins in the frequency range 1-5 HzPartial sum of the frequency bins in the frequency

range 1-5HzFirst sum is between 1-2 Hz, second in 2-3 Hz, etc.

Wavelet packet decompositionThis was done by Fengyu Cong

Feature extraction

Feature selectionFeature 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 selectionOriginally introduced by M. A. Efroymson

1960Tries to find best feature subset

Model includes only significant featuresFeatures are usually evaluated using F-test

Based on linear regressionFeature is significant if it’s f-value >

predetermined significant level

Backward and forward selectionBackward selection

Starts with all features in the modelRemoves features one by one starting from most

unsignificantContinues until model includes only significant

featuresForward selection

Opposite to backward selectionStarts with zero features in the modelAdds features one by one starting from most

significantContinues until all significant features are selected

Genetic algorithmGenetic 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 1975The 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 algorithmCrossover 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 algorithmReproduction 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 analysisPCA was introduced by Karl Pearson in

1901The method was not able to calculate more

than two or three variablesIn 1933 Harold Hotelling described the

methods for computing multivariate PCA

Principal component analysisThe 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 machineVladimir Vapnik introduced SVM in 1995A binary classification toolTries to find optimal separating hyperplane

to separate classes from each otherBasic SVM can classify only two classes but

it can be extended to multiclass classifier

Support vector machineCreates a model based on training data

Each data sample has a class labelModel predicts to which class a specific data

sample belongsModel is tested using testing data

Predicted labels are compared to known labelsA Matlab library LIBSVM was used as an SVM-

toolLIBSVM implements most of the common SVM

methodsSupports multiclass classification

ResultsData was classified with SVM using PCATwo data sets

Set 1 consists of 1779 normal and 12 anomaly samplesSet 2 consists of 1779 normal and 21 anomaly samplesBoth sets have 30 features which are generated with

wavelet packet decomposition70% of the normal samples were used to create SVM

modelRest of the normal samples (534) were used to test the

modelPCA was used to select the features that represents most

of the variation in the dataTests were run 1000 times to get proper results

The results are mean values of 1000 test runs

ResultsSet 1

With three or more principal componentsAll 12 anomalies were classified correctly6.93 normal samples out of 534 were classified incorrectly

Set 2With three principal components

1.63 anomalies out of 21 were classified incorretly7.26 normal samples out of 534 were classified incorrectly

With 10 or more principal components0.02 anomalies out of 21 were classified incorrectly6.53 normal samples out of 534 were classified incorrectly

Results, set 1

Results, set 2

ProblemsTimestamps are not accurate which affects

the labeling of the classesSome normal data samples are labeled as

anomalies and vice versaSmall number of anomaly data samples

compared to normal data samplesFor example data set 1 has 12 anomaly and

1779 normal samplesMulticlass classification is difficult because

there is not enough anomaly samples to create multiclass SVM model

References Backward and forward selection

N. R. Draper and H. Smith, Applied regression analysis 2nd edition, John Wiley & Sons Inc, 1981. 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 John Holland, Adaptation in natural and artificial systems : an introductory analysis with

applications to biology, control, and artificial intelligence, University of Michigan Press, 1975. 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. Darrell Whitley, A genetic algorithm tutorial, in Statistics and computing 4, pages 65-85, 1994.

PCA 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. Ian T. Jolliffe, Principal Component Analysis, Springer-Verlag, New York, 2002. 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 Related Work

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.

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.

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.

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.

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.

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 Corinna Cortes and Vladimir Vapnik, Support-Vector Networks, Machine Learning, Volume 20, pages

273-297, Kluwer Academic Publishers, Boston, 1995. LIBSVM – A library for Support Vector Machines, http://www.csie.ntu.edu.tw/~cjlin/libsvm/ , referred

4.2.2011.

Tack så mycket!