nach oben

Pattern Recognition and Image Analysis

Erschienen in:

01.04.2020 | APPLICATION PROBLEMS

Reduced Featured Based Projective Integral for Road Cracks Detection and Classification

verfasst von: N. Aboutabit

Erschienen in: Pattern Recognition and Image Analysis | Ausgabe 2/2020

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper presents an enhanced and robust approach to detect and classify pavement cracks from captured images. The approach was based on three stages: pre-processing, feature extraction and classification. In pre-processing, we carried out several algorithms to compensate the impact of quality distortions during image acquisition. Then, features are retrieved from projective integrals computed on edge images. These features fed machine learning algorithms to classify the type of crack that may appear in a pavement image. The obtained results proved the relevance of our reduced features. We achieved the best successful classification rate of 93.4% using the Support Vector Machine (SVM) classifier and an accuracy of 94.7% for crack detection.

Vorheriger Artikel Extraction of Vascular Structure in 3D Cardiac CT Images by Using Object/Background Normalization

Nächster Artikel Recognition of Cardiovascular Diseases through Retinal Images Using Optic Cup to Optic Disc Ratio

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

C. Mettas, K. Themistocleous, K. Neocleous, A. Christofe, K. Pilakoutas, and D. Hadjimitsis, “Monitoring asphalt pavement damages using remote sensing techniques,” in Third Int. Conf. on Remote Sensing and Geoinformation of the Environment (RSCy2015), Ed. by D. G. Hadjimitsis, K. Themistocleous, S. Michaelides, and G. Papadavid, Proc. SPIE 9535, 95350S (2015). https://doi.org/10.1117/12.2195702

E. Schnebele, B. F. Tanyu, G. Cervone, and N. Waters, “Review of remote sensing methodologies for pavement management and assessment,” Eur. Transp. Res. Rev. 7, Article 7 (2015). https://doi.org/10.1007/s12544-015-0156-6 CrossRef

S. Zhang and S. M. Bogus, “Use of low-cost remote sensing for infrastructure management,” in Construction Research Congress 2014: Construction in a Global Network (Atlanta, GA, 2014), Ed. by D. Castro-Lacouture, J. Irizarry, and B. Ashuri (ASCE, 2014), pp. 1299–1308. https://doi.org/10.1061/9780784413517.133

A. Cubero-Fernandez, F. J. Rodriguez-Lozano, R. Villatoro, J. Olivares, and J. M. Palomares, “Efficient pavement crack detection and classification,” EURASIP J. Image Video Process. 2017, Article 39 (2017). https://doi.org/10.1186/s13640-017-0187-0 CrossRef

B. Peng, Y.-S. Jiang, and Y. Pu, “Review on automatic pavement crack image recognition algorithms,” J. Highw. Transp. Res. Dev. 9 (2), 13–20 (2015). https://doi.org/10.1061/JHTRCQ.0000435 CrossRef

Y. Huang and B. Xu, “Automatic inspection of pavement cracking distress,” J. Electron. Imaging 15 (1), 013017 (2006). https://doi.org/10.1117/1.2177650 CrossRef

A. Ouyang, C. Luo, and C. Zhou, “Surface distresses detection of pavement based on digital image processing,” in Computer and Computing Technologies in Agriculture IV, CCTA 2010, Ed. D. Li, Y. Liu, and Y. Chen, IFIP Advances in Information and Communication Technology (Springer, Berlin, Heidelberg, 2011), Vol. 347, pp. 368–375. https://doi.org/10.1007/978-3-642-18369-0_42

P. Subirats, J. Dumoulin, V. Legeay, and D. Barba, “Automation of pavement surface crack detection using the continuous wavelet transform,” in Proc. 2006 IEEE Int. Conf. on Image Processing (ICIP 2006) (Atlanta, GA, USA, 2006), pp. 3037–3040.

Q. Zou, Y. Cao, Q. Li, Q. Mao, and S. Wang, “Cracktree: Automatic crack detection from pavement images,” Pattern Recogn. Lett. 33 (3), 227–238 (2012).CrossRef

10.

H. D. Cheng and M. Miyojim, “Automatic pavement distress detection system,” Inf. Sci. 108 (1–4), 219–240 (1998).CrossRef

11.

J. Chou, W. A. O’Neill, and H. D. Cheng, “Pavement distress classification using neural networks,” in Proc. IEEE Int. Conf. on Systems, Man and Cybernetics (San Antonio, TX, USA, 1994), Vol. 1, pp. 397–401.

12.

L. Li, L. Sun, G. Ning, and S. Tan, “Automatic pavement crack recognition based on BP Neural Network,” Promet – Traffic & Transp. 26 (1), 11–22 (2014). https://doi.org/10.7307/ptt.v26i1.1477

13.

Q. Li, Q. Zou, D. Zhang, and Q. Mao, “Fosa: F* Seed-growing Approach for crack-line detection from pavement images,” Image Vision Comput. 29 (12), 861–872 (2011).CrossRef

14.

Z. Fan, Y. Wu, J. Lu, and W. Li, “Automatic pavement crack detection based on structured prediction with the convolutional neural network,” CoRR arXiv preprint arXiv:1802.02208 (2018). https://arxiv.org/abs/1802.02208

15.

G. Deng, L. W. Cahill, and G. R. Tobin, “The study of logarithmic image processing model and its application to image enhancement,” IEEE Trans. Image Process. 4 (4), 506–512 (1995).CrossRef

16.

C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Sixth International Conference on Computer Vision (Bombay, India, 1998) (IEEE/Narosa Publishing House, 1998), pp. 839–846.

17.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8 (6), 679–698 (1986).CrossRef

18.

T. Cover and P. Hart, “Nearest neighbor pattern classification,” IEEE Trans. Inf. Theory 13 (1), 21–27 (1967).CrossRef

19.

C. Cortes and V. Vapnik, “Support-vector networks,” Mach. Learn. 20 (3), 273–297 (1995).MATH

Titel: Reduced Featured Based Projective Integral for Road Cracks Detection and Classification
verfasst von: N. Aboutabit
Publikationsdatum: 01.04.2020
Verlag: Pleiades Publishing
Erschienen in: Pattern Recognition and Image Analysis / Ausgabe 2/2020
Print ISSN: 1054-6618
Elektronische ISSN: 1555-6212
DOI: https://doi.org/10.1134/S1054661820020029

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Springer Professional "Technik"

Weitere Artikel der Ausgabe 2/2020

Topological Data Analysis in Materials Science: The Case of High-Temperature Cuprate Superconductors

Subcircuit Pattern Recognition in Transistor Level Circuits

Image Similarity Estimation Based on Ratio and Distance Calculation between Features

Graphics Pipeline Evolution Based on Object Shaders

Context Modeling in Problems of Compressing Hyperspectral Remote Sensing Data

Decision-making Technology Based on Big Data