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Yıl 2016, , 290 - 295, 01.12.2016
https://doi.org/10.18100/ijamec.270546

Öz

Kaynakça

  • [1] Aksamit P. and Szmechta M. Distributed, mobile, social system for road surface defects detection, In Computational Intelligence and Intelligent Informatics (ISCIII), 2011 5th International Symposium on (pp. 37-40). IEEE, 2011, September.
  • [2] Bello-Salau H., Aibinu A. M., Onwuka E. N., Dukiya J. J., and Onumanyi A. J. Image processing techniques for automated road defect detection: A survey. In Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on pp. 1-4 IEEE 2014, September.
  • [4] Nguyen T. S. , Avila M. and Begot S. Automatic detection and classification of defect on road pavement using anisotropy measure, In Signal Processing Conference, 2009 17th European, pp. 617-621, August 2009.
  • [5] Aydin I., Karaköse M. and Akin E. A robust anomaly detection in pantograph-catenary system based on mean-shift tracking and foreground detection, In 2013 IEEE international conference on systems, man, and cybernetics, pp. 4444-4449, October ,2013.
  • [6] Aydin I., Karaköse E., Karaköse M., Gençoğlu M. T. and Akın E. A new computer vision approach for active pantograph control, In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on ,pp. 1-5, June ,2013.
  • [7] Karakose M. and Baygin M., Image processing based analysis of moving shadow effects for reconfiguration in pv arrays, In Energy Conference (ENERGYCON), 2014 IEEE International, pp. 683-687, May ,2014.
  • [8] Sy N. T., Avila M., Begot S. and Bardet J. C. Detection of Defects in Road Surface by a Vision System, In MELECON 2008-The 14th IEEE Mediterranean Electrotechnical Conference, pp. 847-851, 2008.
  • [9] Zhang D., Qu S., He L. and Shi S. Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence, Optics and Lasers in Engineering, ELSEVIER, vol.47,no.11, pp.1216-1225 November 2009 .
  • [10] Meignen D., Bernadet M., Briand H. One Application of Neural Networks for Detection of Defects Using Video Data Bases: Identification of Road Distresses, In Database and Expert Systems Applications, Proceedings, Eighth International Workshop on IEEE, pp. 459-464 1997.
  • [11] Kumar G. and Bhatia P. K. A detailed review of feature extraction in image processing systems, In 2014 Fourth International Conference on Advanced Computing & Communication Technologies, pp. 5-12, 2014, February, IEEE.
  • [12] Bao G. Road distress analysis using 2D and 3D information (Doctoral dissertation, The University of Toledo) ,2010.
  • [13] Rababaah H., Asphalt Pavement Crack Classification: A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms, and Self-Organizing Maps (Doctoral dissertation, faculty of the University Graduate School in partial fulfillment of the requirements for the degree Master of Sciences in the Department of Computer and Information Sciences, Indiana University South Bend) 2005.
  • [14] Evans T., Semi-automated detection of defects in road surfaces, Monash University, Australia Clayton Campus, 2004.
  • [15] Shi Y., Cui L., Qi Z., Meng F. & Chen Z. Automatic Road Crack Detection Using Random Structured Forests,
  • [16] XU K., WEI N. and MA R. Pavement crack image detection algorithm under nonuniform illuminance, IEEE Third International Conference on Information Science and Technology (ICIST). IEEE, 2013. p. 1281-1284.
  • [17] Azhar, K., Murtaza, F., Yousaf, M. H. & Habib, H. A. Computer vision based detection and localization of potholes in asphalt pavement images, Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on IEEE, November, 2016, pp. 1-5.
  • [18] Amhaz R., Chambon S., Idier J. and Baltazart V. Automatic crack detection on 2D pavement images: An algorithm based on minimal path selection, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, no. 10, October 2016.
  • [19] SALARI E. and BAO G. Automated pavement distress inspection based on 2D and 3D information, Electro/Information Technology (EIT), 2011 IEEE International Conference on. IEEE, 2011. p. 1-4.
  • Sun Y. Automated Pavement Distress Detection Using Advanced Image Processing Techniques, The University of Toledo The University of Toledo Digital Repository, Theses and Dissertations, Canada, 2009.
  • Zhang L., Yang F., Zhang Y. D. & Zhu Y. J. Road crack detection using deep convolutional neural network, Image Processing (ICIP), 2016 IEEE International Conference on IEEE, 2016, September, pp. 3708-3712.
  • Mokri S. S., Saripan M. I., Rahni A. A., Nordin A. J., Hashim S. and Marhaban M. H. PET image reconstruction incorporating 3D mean-median sinogram filtering, IEEE Transactions on Nuclear Science, vol.63, pp.157-169, 2016.
  • Sagar B. D. and Lim S. L. Morphing of Grayscale DEMs via Morphological Interpolations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8(11), pp. 5190-5198, 2015.
  • Geiß C., Klotz M., Schmitt A. and Taubenböck H. Object-Based Morphological Profiles for Classification of Remote Sensing Imagery, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-12,2016.
  • Gundogdu E., Koç A. and Alatan A. A. Infrared object classification using decision tree based deep neural networks, In 2016 24th Signal Processing and Communication Application Conference (SIU), IEEE, 2016, May, pp. 1913-1916, May,2016.
  • Hameed A., Dai R. and Balas B. A Decision-Tree-Based Perceptual Video Quality Prediction Model and Its Application in FEC for Wireless Multimedia Communications, IEEE Transactions on Multimedia, vol.18(4), pp. 764-774, 2016.
  • Baygin M., Karaköse M., A New Image Stitching Approach for Resolution Enhancement in Camera Arrays, The 9th International Conference on Electrical and Electronics Engineering (ELECO 2015), 1186-1190, Bursa, Türkiye, 26-28 November, 2015
  • Santur Y., Karaköse M., Aydın İ., Akın E., IMU Based Adaptive Blur Removal Approach using Image Processing for Railway Inspection, The 23rd International Conference on Systems, Signals and Image Processing (IWSSIP 2016), 23-25 May, 2016.
  • Akarsu B., Parlak K. S., Karaköse M., A Fast Detection Approach for Road Defects Using Image Processing, The 3rd International Conference on Advanced Technology & Sciences (ICAT'16), Eylül, 2016, Konya, Turkey.

A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation

Yıl 2016, , 290 - 295, 01.12.2016
https://doi.org/10.18100/ijamec.270546

Öz

Road defect is one of the most important factors for traffic accident.
Therefore, these defects should be corrected as soon as possible. It usually occurs
cracks, rutting, and potholes in road surface. These errors are based on the
fact that people have recognized and fixed these errors in our day. But if
these errors are not corrected in a short time, the size of the error grows day
by day. There are various methods used to detect road errors in the literature.
One of these methods is the use of computer vision. There are various types of
roads in real life. Since the studies in the literature have been carried out
only by taking into account one type of road, the accuracy rates decrease when
these studies are used in different types of roads. In the study carried out,
different roads have been made adaptive by the operations performed in the
detection of road errors from the received images. Images taken from the camera
on a vehicle are used for the study. The study applied is ensured to have high
accuracy rates in different types of roads via customization. In the second
stage, the image blurred by using median filter and the unprocessed images are
collected, and the darkest parts of the image are brought into the forefront.
The image is converted into a binary image and improved by mathematical
morphological operations. As a result of the operations performed, which of the
five classes including un-cracked roads, superficial crack, crocodile crack,
linear crack and transverse crack the roads belong to is determined. In the
study carried out, the fact that it is fast and that its accuracy rates are
good indicate that it can be used in real life.

Kaynakça

  • [1] Aksamit P. and Szmechta M. Distributed, mobile, social system for road surface defects detection, In Computational Intelligence and Intelligent Informatics (ISCIII), 2011 5th International Symposium on (pp. 37-40). IEEE, 2011, September.
  • [2] Bello-Salau H., Aibinu A. M., Onwuka E. N., Dukiya J. J., and Onumanyi A. J. Image processing techniques for automated road defect detection: A survey. In Electronics, Computer and Computation (ICECCO), 2014 11th International Conference on pp. 1-4 IEEE 2014, September.
  • [4] Nguyen T. S. , Avila M. and Begot S. Automatic detection and classification of defect on road pavement using anisotropy measure, In Signal Processing Conference, 2009 17th European, pp. 617-621, August 2009.
  • [5] Aydin I., Karaköse M. and Akin E. A robust anomaly detection in pantograph-catenary system based on mean-shift tracking and foreground detection, In 2013 IEEE international conference on systems, man, and cybernetics, pp. 4444-4449, October ,2013.
  • [6] Aydin I., Karaköse E., Karaköse M., Gençoğlu M. T. and Akın E. A new computer vision approach for active pantograph control, In Innovations in Intelligent Systems and Applications (INISTA), 2013 IEEE International Symposium on ,pp. 1-5, June ,2013.
  • [7] Karakose M. and Baygin M., Image processing based analysis of moving shadow effects for reconfiguration in pv arrays, In Energy Conference (ENERGYCON), 2014 IEEE International, pp. 683-687, May ,2014.
  • [8] Sy N. T., Avila M., Begot S. and Bardet J. C. Detection of Defects in Road Surface by a Vision System, In MELECON 2008-The 14th IEEE Mediterranean Electrotechnical Conference, pp. 847-851, 2008.
  • [9] Zhang D., Qu S., He L. and Shi S. Automatic ridgelet image enhancement algorithm for road crack image based on fuzzy entropy and fuzzy divergence, Optics and Lasers in Engineering, ELSEVIER, vol.47,no.11, pp.1216-1225 November 2009 .
  • [10] Meignen D., Bernadet M., Briand H. One Application of Neural Networks for Detection of Defects Using Video Data Bases: Identification of Road Distresses, In Database and Expert Systems Applications, Proceedings, Eighth International Workshop on IEEE, pp. 459-464 1997.
  • [11] Kumar G. and Bhatia P. K. A detailed review of feature extraction in image processing systems, In 2014 Fourth International Conference on Advanced Computing & Communication Technologies, pp. 5-12, 2014, February, IEEE.
  • [12] Bao G. Road distress analysis using 2D and 3D information (Doctoral dissertation, The University of Toledo) ,2010.
  • [13] Rababaah H., Asphalt Pavement Crack Classification: A Comparative Study of Three AI Approaches: Multilayer Perceptron, Genetic Algorithms, and Self-Organizing Maps (Doctoral dissertation, faculty of the University Graduate School in partial fulfillment of the requirements for the degree Master of Sciences in the Department of Computer and Information Sciences, Indiana University South Bend) 2005.
  • [14] Evans T., Semi-automated detection of defects in road surfaces, Monash University, Australia Clayton Campus, 2004.
  • [15] Shi Y., Cui L., Qi Z., Meng F. & Chen Z. Automatic Road Crack Detection Using Random Structured Forests,
  • [16] XU K., WEI N. and MA R. Pavement crack image detection algorithm under nonuniform illuminance, IEEE Third International Conference on Information Science and Technology (ICIST). IEEE, 2013. p. 1281-1284.
  • [17] Azhar, K., Murtaza, F., Yousaf, M. H. & Habib, H. A. Computer vision based detection and localization of potholes in asphalt pavement images, Electrical and Computer Engineering (CCECE), 2016 IEEE Canadian Conference on IEEE, November, 2016, pp. 1-5.
  • [18] Amhaz R., Chambon S., Idier J. and Baltazart V. Automatic crack detection on 2D pavement images: An algorithm based on minimal path selection, IEEE Transactions on Intelligent Transportation Systems, Vol. 17, no. 10, October 2016.
  • [19] SALARI E. and BAO G. Automated pavement distress inspection based on 2D and 3D information, Electro/Information Technology (EIT), 2011 IEEE International Conference on. IEEE, 2011. p. 1-4.
  • Sun Y. Automated Pavement Distress Detection Using Advanced Image Processing Techniques, The University of Toledo The University of Toledo Digital Repository, Theses and Dissertations, Canada, 2009.
  • Zhang L., Yang F., Zhang Y. D. & Zhu Y. J. Road crack detection using deep convolutional neural network, Image Processing (ICIP), 2016 IEEE International Conference on IEEE, 2016, September, pp. 3708-3712.
  • Mokri S. S., Saripan M. I., Rahni A. A., Nordin A. J., Hashim S. and Marhaban M. H. PET image reconstruction incorporating 3D mean-median sinogram filtering, IEEE Transactions on Nuclear Science, vol.63, pp.157-169, 2016.
  • Sagar B. D. and Lim S. L. Morphing of Grayscale DEMs via Morphological Interpolations, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8(11), pp. 5190-5198, 2015.
  • Geiß C., Klotz M., Schmitt A. and Taubenböck H. Object-Based Morphological Profiles for Classification of Remote Sensing Imagery, IEEE Transactions on Geoscience and Remote Sensing, pp. 1-12,2016.
  • Gundogdu E., Koç A. and Alatan A. A. Infrared object classification using decision tree based deep neural networks, In 2016 24th Signal Processing and Communication Application Conference (SIU), IEEE, 2016, May, pp. 1913-1916, May,2016.
  • Hameed A., Dai R. and Balas B. A Decision-Tree-Based Perceptual Video Quality Prediction Model and Its Application in FEC for Wireless Multimedia Communications, IEEE Transactions on Multimedia, vol.18(4), pp. 764-774, 2016.
  • Baygin M., Karaköse M., A New Image Stitching Approach for Resolution Enhancement in Camera Arrays, The 9th International Conference on Electrical and Electronics Engineering (ELECO 2015), 1186-1190, Bursa, Türkiye, 26-28 November, 2015
  • Santur Y., Karaköse M., Aydın İ., Akın E., IMU Based Adaptive Blur Removal Approach using Image Processing for Railway Inspection, The 23rd International Conference on Systems, Signals and Image Processing (IWSSIP 2016), 23-25 May, 2016.
  • Akarsu B., Parlak K. S., Karaköse M., A Fast Detection Approach for Road Defects Using Image Processing, The 3rd International Conference on Advanced Technology & Sciences (ICAT'16), Eylül, 2016, Konya, Turkey.
Toplam 28 adet kaynakça vardır.

Ayrıntılar

Konular Mühendislik
Bölüm Research Article
Yazarlar

Büşra Akarsu

Mehmet Karaköse

Koray Parlak Bu kişi benim

Erhan Akın

Alişan Sarımaden

Yayımlanma Tarihi 1 Aralık 2016
Yayımlandığı Sayı Yıl 2016

Kaynak Göster

APA Akarsu, B., Karaköse, M., Parlak, K., Akın, E., vd. (2016). A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics Electronics and Computers(Special Issue-1), 290-295. https://doi.org/10.18100/ijamec.270546
AMA Akarsu B, Karaköse M, Parlak K, Akın E, Sarımaden A. A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics Electronics and Computers. Aralık 2016;(Special Issue-1):290-295. doi:10.18100/ijamec.270546
Chicago Akarsu, Büşra, Mehmet Karaköse, Koray Parlak, Erhan Akın, ve Alişan Sarımaden. “A Fast and Adaptive Road Defect Detection Approach Using Computer Vision With Real Time Implementation”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1 (Aralık 2016): 290-95. https://doi.org/10.18100/ijamec.270546.
EndNote Akarsu B, Karaköse M, Parlak K, Akın E, Sarımaden A (01 Aralık 2016) A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 290–295.
IEEE B. Akarsu, M. Karaköse, K. Parlak, E. Akın, ve A. Sarımaden, “A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation”, International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, ss. 290–295, Aralık 2016, doi: 10.18100/ijamec.270546.
ISNAD Akarsu, Büşra vd. “A Fast and Adaptive Road Defect Detection Approach Using Computer Vision With Real Time Implementation”. International Journal of Applied Mathematics Electronics and Computers Special Issue-1 (Aralık 2016), 290-295. https://doi.org/10.18100/ijamec.270546.
JAMA Akarsu B, Karaköse M, Parlak K, Akın E, Sarımaden A. A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics Electronics and Computers. 2016;:290–295.
MLA Akarsu, Büşra vd. “A Fast and Adaptive Road Defect Detection Approach Using Computer Vision With Real Time Implementation”. International Journal of Applied Mathematics Electronics and Computers, sy. Special Issue-1, 2016, ss. 290-5, doi:10.18100/ijamec.270546.
Vancouver Akarsu B, Karaköse M, Parlak K, Akın E, Sarımaden A. A Fast and Adaptive Road Defect Detection Approach Using Computer Vision with Real Time Implementation. International Journal of Applied Mathematics Electronics and Computers. 2016(Special Issue-1):290-5.