Data mining for pothole detection Pro gradu seminar 10.2.2011

1. Data mining for pothole detectionPro gradu seminar10.2.2011 Hannu Hautakangas Jukka Nieminen

2. Contents • Introduction • Related work • Data • Data preprocessing • Feature extraction • Feature selection • Support vector machine • Results • Problems • References

3. Introduction • Purpose of the research is to detect anomalies on road surface • Expansion joints • Potholes • Speed bumps • Etc. • Supervisors • Tapani Ristaniemi • Fengyu Cong

4. Related work • Accelerometer based techniques • Pothole Patrol • Nericell • Terrain classification • Other techniques • Image detection • Laser profilometer • Ground penetrating radar

5. 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

6. 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]

7. Original and filtered Y-axis data

8. Feature extraction • Several different features were extracted • 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 • First sum is between 1-2 Hz, second in 2-3 Hz, etc. • Wavelet packet decomposition • This was done by Fengyu Cong

9. Feature extraction

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. 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 Z1displays 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

18. 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

19. 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

20. 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

21. 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

22. Results, set 1

23. Results, set 2

24. 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

25. 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.

26. 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.

27. Tack så mycket!