Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques

  • Oladimeji Adeyemi Department of Computer Science, Nigerian Defence Academy, Kaduna
  • Martins Irhebhude Department of Computer Science, Nigerian Defence Academy, Kaduna
  • Adeola Kolawole Department of Computer Science, Nigerian Defence Academy, Kaduna

Abstract

This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

References

(1) Andersen, J., & Sutcliffe, S. (2000). Intelligent Transport Systems (ITS) - An Overview. IFAC Proceedings Volumes, 33(18), 99-106.

(2) Devapriya, W., Nelson Kennedy Babu, C. and Srihari, T. (2016) Real Time Speed Bump Detection Using Gaussian Filtering and Connected Component Approach. Circuits and Systems, 7, 2168-2175.

(3) Brookhuis, K. A., Waard, D. D., & Janssen, W. H. (2001). Behavioural impacts of Advanced Driver Assistance Systems–an overview. EJTIR, 3(1), 245-253.

(4) Devapriya, W., Babu, C. N., & Srihari, T. (2015). Advance Driver Assistance System (ADAS) - Speed bump detection. 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), 478-483.

(5) Reddy, V. K., & Nagesh, B. S. (2016). Smart Phone Based Speed Breaker Early Warning System. International Journal of Computer Science and Information Security (IJCSIS), 14, 20-25.

(6) Rahayu, E., Faizal, M., & Ani, Z. C. (2016). A Vision Based Speed Breaker Detection for Early Warning Notification. 43-47.

(7) Optical Character Recognition (OCR) - File Exchange - MATLAB Central. Retrieved October 18, 2018, from https://www.mathworks.com/matlabcentral/fileexchange/18169-optical-character-recognition-ocr

(8) Celaya-Padilla, J., Galván-Tejada, C., López-Monteagudo, F., Alonso-González, O., Moreno-Báez, A., Martínez-Torteya, A., . . . Gamboa-Rosales, H. (2018). Speed Bump Detection Using Accelerometric Features: A Genetic Algorithm Approach. Sensors, 18(2), 443.

(9) P, M., Singh, S., Shukla, S., & Krishnan, U. (2017). detection of humps and potholes on roads and notifying the same to the drivers. International Journal of Management and Applied Science, 3(1), 130-133.

(10) Danti, A., Dr, Kulkarni, J. Y., Smt, & Hiremath, P. S., Dr. (2013). A Technique for Bump Detection in Indian Road Images Using Color Segmentation and Knowledge Base Object Detection. International Journal of Scientific & Engineering Research, 4(8).

(11) Afrin, M., Mahmud, M. R., & Razzaque, M. A. (2015). Real time detection of speed breakers and warning system for on-road drivers. 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE), 495-498.

(12) Chugh, G., Bansal, D. and Sofat, S. (2014) Road Condition Detection Using Smartphone Sensors: A Survey. International Journal of Electronic and Electrical Engineering, 7, 595-601.

(13) Manikandan , B., & Bharathi, M. (2018). SPEED BREAKER DETECTION USING BLOB ANALYSIS. International Journal of Pure and Applied Mathematics, 118(20), 3671–3677.

(14) Anusha, K. & Sirisha, K. (2011) “Breaking the Speed Breakers Using Image Processing” Retrieved from https://www.scribd.com/document/54957257/Breaking-the-Speed-Breakers-Using-Image-Processing-1-2

(15) Sagar, B. M., G, Shobha., & P. Ramakanth Kumar. (2008). OCR for printed Kannada text to Machine editable format using Database approach. WSEAS Transactions on Computers, 7(6), 766–769.

(16) Mithe, R., Indalkar, S., & Divekar, N. (2013). Optical Character Recognition. International Journal of Recent Technology and Engineering, 2(1), 72–75.

(17) Sadasivan, A. K., & Senthilkumar, T. (2012). Automatic Character Recognition in Complex Images. Procedia Engineering, 30, 218–225.

(18) Tiwari, S., Mishra, S., Bhatia, P., & Yadav, P. K. (2013). Optical Character Recognition using MATLAB. International Journal of Advanced Research in Electronics and Communication Engineering, 2(5), 579–582.

(19) Singla, S., & Yadav, R. (2014). Optical Character Recognition Based Speech Synthesis System Using LabVIEW. Journal of Applied Research and Technology, 12(5), 919–926.

(20) Greenhalgh, J., & Mirmehdi, M. (2015). Recognizing Text-Based Traffic Signs. IEEE Transactions on Intelligent Transportation Systems, 16(3), 1360–1369.

(21) Huang, D., Shan, C., Ardabilian, M., Wang, Y., & Chen, L. (2011). Local Binary Patterns and Its Application to Facial Image Analysis: A Survey. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 41(6), 765–781.

(22) Irhebhude, M. E., Nawahda, A., & Edirisinghe, E. A. (2016). View invariant vehicle type recognition and counting system using multiple features. International Journal of Computer Vision and Signal Processing, 6(1), 20-32.

(23) Brahnam, S., Jain, L. C., Lumini, A., & Nanni, L. (2013). Introduction to Local Binary Patterns: New Variants and Applications. Local Binary Patterns: New Variants and Applications Studies in Computational Intelligence, 1–13.

(24) Pupale, R. (2019, February 11). Support Vector Machines(SVM) - An Overview. Retrieved August 9, 2019, from https://towardsdatascience.com/https-medium-com-pupalerushikesh-svm-f4b42800e989

(25) Cortes, C., & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.

(26) Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415–425.

(27) Gonzalez, C. & Woods, R.E. (2013) Digital Image Processing, Person. 3rd Edition, Person, New Delhi, 635, 738.

(28) Marita, T., Negru, M., Danescu, R., & Nedevschi, S. (2011). Stop-line detection and localization method for intersection scenarios. 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing, 293–298.

Published
2019-11-08