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  • 1.
    Li, Hongxia
    et al.
    Changan Univ, Peoples R China; Baoji Univ Arts & Sci, Peoples R China.
    Wang, Weixing
    Changan Univ, Peoples R China.
    Wang, Mengfei
    Changan Univ, Peoples R China.
    Li, Limin
    Wenzhou Univ, Peoples R China.
    Vimarlund, Vivian
    Linköpings universitet, Institutionen för datavetenskap, Interaktiva och kognitiva system. Linköpings universitet, Tekniska fakulteten.
    A review of deep learning methods for pixel-level crack detection2022Ingår i: Journal of Traffic and Transportation Engineering (English Edition), ISSN 2095-7564, Vol. 9, nr 6, s. 945-968Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    Cracks are a major sign of aging transportation infrastructure. The detection and repair of cracks is the key to ensuring the overall safety of the transportation infrastructure. In recent years, due to the remarkable success of deep learning (DL) in the field of crack detection, many researches have been devoted to developing pixel-level crack image seg-mentation (CIS) models based on DL to improve crack detection accuracy, but as far as we know there is no review of DL-based CIS methods yet. To address this gap, we present a comprehensive thematic survey of DL-based CIS techniques. Our review offers several contributions to the CIS area. First, more than 40 papers of journal or top conference most published in the last three years are identified and collected based on the systematic literature review method. Second, according to the backbone network architecture of the models proposed in them, they are grouped into 10 topics: FCN, U-Net, encoder-decoder model, multi-scale, attention mechanism, transformer, two-stage detection, multi-modal fusion, unsupervised learning and weakly supervised learning, to be reviewed. Meanwhile, our survey focuses on discussing strengths and limitations of the models in each topic so as to reveal the latest research progress in the CIS field. Third, publicly accessible data sets, evaluation metrics, and loss functions that can be used for pixel-level crack detection are systematically introduced and summarized to facilitate researchers to select suitable components according to their own research tasks. Finally, we discuss six common problems and existing solutions to them in the field of DL-based CIS, and then suggest eight possible future research directions in this field. (c) 2022 Periodical Offices of Changan University. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC -ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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  • 2.
    Wang, Weixing
    et al.
    Royal Institute of Technology, Stockholm, Sweden; School of Information Engineering, Chang’an University, Xi’an, Shaanxi, China.
    Zhao, Weisen
    School of Information Engineering, Chang’an University, Xi’an, Shaanxi, China.
    Huang, Lingxiao
    School of Information Engineering, Chang’an University, Xi’an, Shaanxi, China.
    Vimarlund, Vivian
    Linköpings universitet, Institutionen för datavetenskap, Interaktiva och kognitiva system. Linköpings universitet, Tekniska högskolan.
    Wang, Zhiwei
    School of Information Engineering, Chang’an University, Xi’an, Shaanxi, China.
    Applications of terrestrial laser scanning for tunnels: a review2014Ingår i: Journal of Traffic and Transportation Engineering (English Edition), ISSN 2095-7564, Vol. 1, nr 5, s. 325-337Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In recent years, the use of terrestrial laser scanning (TLS) technique in engineering surveys is gaining an increasing interest due to the advantages of non-contact, rapidity, high accuracy, and large scale. Millions of accurate 3D points (mm level accuracy) can be delivered by this technique with a high point density in a short time (up to 1 million points per second), which makes it a potential technique for large scale applications in engineering environments such as tunnels, bridges, and heritage buildings. Tunnels, in particular those with long lengths, create great challenges for surveyors to obtain the satisfactory scanned data. This paper presents a short history of TLS techniques used for tunnels. A general overview of TLS techniques is given, followed by a review of several applications of TLS for tunnels. These applications are classified as: detecting geological features of drilling tunnels, monitoring the geometry of tunnels during excavation, making deformation measurements, and extracting features. The review emphasizes how TLS techniques can be used to measure various aspects of tunnels. It is clear that TLS techniques are not yet a common tool for tunnel investigations, but there is still a huge potential to excavate.

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