Pavement Cracks Coupled With Shadows: A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

Fan, Lili; Shen, Li; Liu, Ying; Bai, Ligang; Cao, Dongpu; Wang, Fei‐Yue

doi:10.1109/jas.2023.123447

Cited by 22 publications

(1 citation statement)

References 43 publications

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“…Fan et al [75]. developed a two-step shadow removal-oriented crack detection (SROCD) method that focuses on removing shadows before finding cracks.…”

Section: Detectionmentioning

confidence: 99%

How to Make a State of the Art Report—Case Study—Image-Based Road Crack Detection: A Scientometric Literature Review

Fan,

Tang,

Mohd Ariffin

et al. 2024

Applied Sciences

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With the rapid growth in urban construction in Malaysia, road breakage has challenged traditional manual inspection methods. In order to quickly and accurately detect the extent of road breakage, it is crucial to apply automated road crack detection techniques. Researchers have long studied image-based road crack detection techniques, especially the deep learning methods that have emerged in recent years, leading to breakthrough developments in the field. However, many issues remain in road crack detection methods using deep learning techniques. The field lacks state-of-the-art systematic reviews that can scientifically and effectively analyze existing works, document research trends, summarize outstanding research results, and identify remaining shortcomings. To conduct a systematic review of the relevant literature, a bibliometric analysis and a critical analysis of the papers published in the field were performed. VOSviewer and CiteSpace text mining tools were used to analyze and visualize the bibliometric analysis of some parameters derived from the articles. The history and current status of research in the field by authors from all over the world are elucidated and future trends are analyzed.

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“…Fan et al [75]. developed a two-step shadow removal-oriented crack detection (SROCD) method that focuses on removing shadows before finding cracks.…”

Section: Detectionmentioning

confidence: 99%