Rmo Automated Trend Modules Crack Extraction In

Automatic crack extraction in pavement images is still a challenging topic.

Abstract The extraction of pavement cracks is always a hard task in image processing.

In airport and road construction, cracking is the main factor for pavement damage, which can decrease the quality of pavement and affect transportation seriously.

Cracks also exist in other artificial or natural objects, such as buildings, bridges, tunnels, etc.

Among all the object images, pavement crack images are the most complex, so the image processing and analysis for them is harder than other crack images.

From the early image acquisition based on photography technology to the current 3D laser scanning technology, the pavement crack image acquisition technology is becoming more convenient and efficient, but there are still challenges in the automatic processing and recognition of cracks in images.

From the early global thresholding to deep learning algorithms, the research for crack extraction has been developed for about 40 years.

Therefore, in order to know the developing history and the advanced research, we have collected a number of literature in this research topic for summarizing the research artwork status, and giving a review of the pavement crack image acquisition methods and 2D crack extraction algorithms.

Also, for image acquisition methods and pavement crack image segmentation, more detailed comparison and discussions are made.

Previous article in issue Next article in issue Keywords Highway engineering Pavement crack Image acquisition Image processing Crack extraction Recommended articles Citing articles (0) Wang Weixing is a professor in information engineering.

He obtained his PhD degree in 1997 at Royal Institute of Technology, Sweden, where, since 2001 until now, he has been working as a PhD supervisor.

His research interests include information engineering, image processing and analysis, pattern recognition and computer vision.

Peer review under responsibility of Periodical Offices of Changan University.

Publishing services by Elsevier B.V.

Owner. Recommended articles No articles found.

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Rmo Automated Trend Modules Cracked