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Article

CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes

1
Future Infrastructure Research Center, Korea Institute of Civil Engineering and Building Technology (KICT), Goyang 10223, Korea
2
Department of Computer Engineering, Chosun University, Gwangju 61452, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Danda B. Rawat
Electronics 2021, 10(12), 1402; https://doi.org/10.3390/electronics10121402
Received: 5 May 2021 / Revised: 29 May 2021 / Accepted: 8 June 2021 / Published: 10 June 2021
(This article belongs to the Special Issue Wireless Sensor Networks in Intelligent Transportation Systems)
Poor road-surface conditions pose a significant safety risk to vehicle operation, especially in the case of autonomous vehicles. Hence, maintenance of road surfaces will become even more important in the future. With the development of deep learning-based computer image processing technology, artificial intelligence models that evaluate road conditions are being actively researched. However, as the lighting conditions of the road surface vary depending on the weather, the model performance may degrade for an image whose brightness falls outside the range of the learned image, even for the same road. In this study, a semantic segmentation model with an autoencoder structure was developed for detecting road surface along with a CNN-based image preprocessing model. This setup ensures better road-surface crack detection by adjusting the image brightness before it is input into the road-crack detection model. When the preprocessing model was applied, the road-crack segmentation model exhibited consistent performance even under varying brightness values. View Full-Text
Keywords: road-surface cracks; image recognition; artificial intelligence; brightness; semantic segmentation road-surface cracks; image recognition; artificial intelligence; brightness; semantic segmentation
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MDPI and ACS Style

Lee, T.; Yoon, Y.; Chun, C.; Ryu, S. CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes. Electronics 2021, 10, 1402. https://doi.org/10.3390/electronics10121402

AMA Style

Lee T, Yoon Y, Chun C, Ryu S. CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes. Electronics. 2021; 10(12):1402. https://doi.org/10.3390/electronics10121402

Chicago/Turabian Style

Lee, Taehee, Yeohwan Yoon, Chanjun Chun, and Seungki Ryu. 2021. "CNN-Based Road-Surface Crack Detection Model That Responds to Brightness Changes" Electronics 10, no. 12: 1402. https://doi.org/10.3390/electronics10121402

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