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- W4387162246 abstract "Nowadays, it is crucial to continuously monitor and provide real-time analysis to reduce road-related accidents. Road cracks and practices occur on the daily basis on the road. As a result, a review of the numerous method and techniques for crack detection, recognition, and identification was undertaken. In this analysis, 20 to 25 articles were examined that which are published in the last 10 years, from 2012 to 2022. Based on the research, it was found that the Deep CNN is the most optimum method for crack categorization. The motivation of this review is that none of the models are combined into a single model, so a comprehensive list of all these models may be helpful to anyone conducting the study in this area. After reviewing the articles and datasets of the problem, a dataset was proposed that might be used in the field for more research. Finally, it was observed that using deep learning and computer vision or combining both technologies will create intelligent road crack detection is a promising research area." @default.
- W4387162246 created "2023-09-30" @default.
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- W4387162246 date "2023-04-21" @default.
- W4387162246 modified "2023-09-30" @default.
- W4387162246 title "Crevice Identification: A Survey on Surface Monitoring" @default.
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- W4387162246 doi "https://doi.org/10.1109/inc457730.2023.10263072" @default.
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