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Yol Yüzey Anormalisinin Tespiti ve Analizi

Year 2021, Volume: 10 Issue: 3, 1187 - 1194, 17.09.2021

Erkan Deveci Burhan Ergen

https://doi.org/10.17798/bitlisfen.942386

Abstract

Kentleşmenin şehirlerin hızlı gelişmesine neden olması gelişen şehirlerin alt yapılarının takibi ve güncellenme gerekliliğinin analizini zorunlu kılmıştır. Özellikle ulaşım karmaşasının önüne geçmek için çeşitli trafik planlamaları yapmanın yanı sıra ulaşımın sağlandığı kara yolunun niteliğinin de yeterli seviyede olması gereklidir. Yolun yapısal kusurların ve çatlaklarının manuel görsel muayenesi verinin hacmi ve yapının boyutu nedeniyle çok zaman alan zahmetli bir süreçtir. Yoldaki çatlakların ve kusurların manuel olarak incelenmesi, yorgunluk, sorumsuz denetim, zayıf göz görme gibi bir dizi nedenlerden dolayı insan hatası nedeniyle yeterli seviyede değerlendirilememektedir. Yol kusurlarının belirlenmesi, sürücüler için önemli olmakla beraber yaya gibi tüm yol kullanıcılarının güvenliği ve konforunu sağlamak için çukurlar, hız tümsekleri vb. yol yüzeyi anormalliklerinin izlenmesi büyük önem taşımaktadır. Bu çalışmada yol yüzey kalitesinin izlenmesine ve sürücülere daha güvenli bir yol sunmak adına kullanışlı bir otomatik algılama sisteminin geliştirilmesine odaklanmaktadır. Veri seti kamera sayesinde alınan verilerle oluşturulmuştur. Verilerin önişleme fazı tamamlandıktan sonra VGG kullanılarak sonuçlar alınmış, kazanç ve kayıp grafikleri çizdirilmiş ve tahminler yapılmıştır.

Keywords

Derin Öğrenme Karayolu Anormalisi Yol Çatlağı

References

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Year 2021, Volume: 10 Issue: 3, 1187 - 1194, 17.09.2021

Erkan Deveci Burhan Ergen

https://doi.org/10.17798/bitlisfen.942386

Abstract

References

  • [1] Koch C., Georgieva K., Kasireddy V., Akinci B., Fieguth P. 2015. A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure, Advanced Engineering Informatics, 29 (2): 196–210.
  • [2] Cubero-Fernandez A., Rodriguez-Lozano F.J., Villatoro R. et al. 2017. Efficient pavement crack detection and classification. J Image Video Proc. 2017, 39.
  • [3] Zakeri H., Nejad F. M., Fahimifar A. 2017. Image based techniques for crack detection, classication and quantication in asphalt pavement: a review, Archives of Computational Methods in Engineering, 24 (4): 935–977.
  • [4] Tsai Y.-C., Kaul V., Mersereau R. M. 2010. Critical assessment of pavement distress segmentation methods, Journal of Transportation Engineering, 136 (1): 11–19.
  • [5] Koch C., Brilakis I. 2011. Pothole detection in asphalt pavement images, Advanced Engineering Informatics, 25 (3): 507- 515.
  • [6] Yang X., Li H., Yu Y., Luo X., Huang T., Yang X. 2018. Automatic Pixel-Level Crack Detection and Measurement Using Fully Convolutional Network.
  • [7] Futao Ni, Jian Zhang, Zhiqiang Chen,. (2018). Pixel‐level crack delineation in images with convolutional feature fusion. Structural Control and Health Monitoring. 26. 10.1002/stc.2286.
  • [8] Ronneberger O., Fischer P., Brox T. 2015. U-net: Convolutional networks for biomedical image segmentation, International Conference on Medical image computing and computer-assisted intervention, Springer, pp.234–241.
  • [9] Li P., Wang C., Li S., Feng, B. (2016). Research on crack detection method of airport runway based on twice-threshold segmentation, Proceedings - 5th International Conference on Instrumentation and Measurement, Computer, Communication, and Control, IMCCC 2015, 1716–1720.
  • [10] Zou Q., Cao Y., Li Q., Mao Q., Wang S. CrackTree: Automatic crack detection from pavement images, Pattern Recognition Letters, (3): 227–238.
  • [11] Oliveira H., Correia P.L. 24–28 August 2009. Automatic road crack segmentation using entropy and image dynamic thresholding. In Proceedings of the 17th European Signal Processing Conference, Glasgow, Scotland, UK; pp. 622–626.
  • [12] Zhao H., Qin G., Wang X. 16–18 October 2010. Improvement of canny algorithm based on pavement edge detection. In Proceedings of the 2010 3rd International Congress on Image and Signal Processing, CISP 2010, Yantai, China; Volume 2, pp. 964–967.
  • [13] Attoh-Okine N., Ayenu-Prah A. 2008. Evaluating pavement cracks with bidimensional empirical mode decomposition. EURASIP J. Adv. Signal Process. 1–7.
  • [14] Tanaka N., Uematsu K. 17– 19 November 1998. A Crack Detection Method in Road Surface Images Using Morphology. In Proceedings of the Workshop on Machine Vision Applications, Chiba, Japan; 98: 1–4.
  • [15] Medina Roberto, Gayubo Fernando, González Luis M., Olmedo David, Gómez-García-Bermejo Jaime, Zalama Eduardo, Perán José. (2008). Surface Defects Detection on Rolled Steel Strips by Gabor Filters.. VISAPP 2008 - 3rd International Conference on Computer Vision Theory and Applications, Proceedings. 1. 479-485.
  • [16] Kumar Jatinder, Kumar Anish., 2015. Surface crack density and recast layer thickness analysis in WEDM process through response surface methodology. Machining Science and Technology. Article in Press. 10.1080/10910344.2016.1165835.
  • [17] Pawangfg. 2020. VGG-16 CNN Model. https://www.geeksforgeeks.org/vgg-16-cnn-model. (Erişim Tarihi: 21.05.2021).
  • [18] Vedat T, Burhan E. 2019. Intersections and crosswalk detection using deep learning and image processing techniques. Physica A: Statistical Mechanics and its Applications. 543.10.1016/j.physa.2019.123510
There are 18 citations in total.

Details

Primary Language Turkish
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Erkan Deveci 0000-0002-3985-4156

Burhan Ergen 0000-0003-3244-2615

Publication Date September 17, 2021
Submission Date May 24, 2021
Acceptance Date August 18, 2021
Published in Issue Year 2021 Volume: 10 Issue: 3

Cite

IEEE E. Deveci and B. Ergen, “Yol Yüzey Anormalisinin Tespiti ve Analizi”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 10, no. 3, pp. 1187–1194, 2021, doi: 10.17798/bitlisfen.942386.
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Bitlis Eren University
Journal of Science Editor
Bitlis Eren University Graduate Institute
Bes Minare Mah. Ahmet Eren Bulvari, Merkez Kampus, 13000 BITLIS