FPGA-Based Road Crack Detection Using Deep Learning-Reference-Cited by-同舟云学术

FPGA-Based Road Crack Detection Using Deep Learning

Published:2022-09-04 Issue: Volume: Page:65-73
ISSN:2367-3370
Container-title:Lecture Notes in Networks and Systems
language:
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Author:

Canese Lorenzo^ORCID,Cardarilli Gian Carlo^ORCID,Di Nunzio Luca^ORCID,Fazzolari Rocco^ORCID,Re Marco^ORCID,Spanò Sergio^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-16281-7_7

Reference19 articles.

1. Fan, R., Liu, Y., Yang, X., Bocus, M.J., Dahnoun, N., Tancock, S.: Real-time stereo vision for road surface 3-d reconstruction. In: 2018 IEEE International Conference on Imaging Systems and Techniques (IST), pp. 1–6. IEEE (2018)

2. Oliveira, H., Correia, P.L.: Automatic road crack segmentation using entropy and image dynamic thresholding. In: 2009 17th European Signal Processing Conference, pp. 622–626. IEEE (2009)

3. Nguyen, T.S., Avila, M., Begot, S.: Automatic detection and classification of defect on road pavement using anisotropy measure. In: 2009 17th European Signal Processing Conference, pp. 617–621. IEEE (2009)

4. Zhan, J., Dong, S., Hu, W.: IOE-supported smart logistics network communication with optimization and security. Sustain. Energy Technol. Assess. 52, 102052 (2022)

5. Oliveira, H., Correia, P.L.: Automatic road crack detection and characterization. IEEE Trans. Intell. Transp. Syst. 14(1), 155–168 (2012)

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