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| Bimonthly Since 1986 |
ISSN 1004-9037
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| Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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Abstract
Roads play a vital role in transporting the passengers/goods for short and medium distances. The objective of this research is to use video or image sources to detect road surface damage. Utilizing Image Augmentation and Annotation Label Smoothing techniques can increase the effectiveness and precision of spotting damage to the road's surface. This research paper includes investigating data preparation techniques, annotating data labels, and providing a road damage dataset with a training model. Tamil-Nadu-Road-Surface-Dataset-2023(TNRSD2023) is a road surface dataset that was compiled in the area surrounding the Cuddalore district in Tamil Nadu, India by the author.
Keyword
Road Surface damage detection, Image Augmentation, Label Smoothing
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