Arbitrarily-oriented tunnel lining defects detection from Ground Penetrating Radar images using deep Convolutional Neural networks
نویسندگان
چکیده
Tunnel lining internal defect detection is essential for the safe operation of tunnels. This paper presents an automatic scheme based on rotational region deformable convolutional neural network (R 2 DCNN) and Ground Penetrating Radar (GPR) images accurate defects rebars with arbitrary orientations. The R DCNN comprises inter-related modules, specifically, convolution, feature fusion, rotated modules. In this study, synthetic GPR images, including various structural different permittivities, as well real obtained by model experiments, were constructed DCNN. Improved results while testing in comparative experiments. mean average precision was enhanced 8.21% compared to CNN images. showed satisfactory on-site which demonstrated applicability practical • designed utilized detecting tunnel from Rotational proposed. Internal orientations detected using method. superiority method firstly verified also validated
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ژورنال
عنوان ژورنال: Automation in Construction
سال: 2022
ISSN: ['1872-7891', '0926-5805']
DOI: https://doi.org/10.1016/j.autcon.2021.104044