Journal article 1067 views
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images
IEEE Transactions on Intelligent Transportation Systems, Volume: 23, Issue: 11, Pages: 22156 - 22165
Swansea University Author:
Yue Hou
Full text not available from this repository: check for access using links below.
DOI (Published version): 10.1109/tits.2022.3160524
Abstract
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images
| Published in: | IEEE Transactions on Intelligent Transportation Systems |
|---|---|
| ISSN: | 1524-9050 1558-0016 |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2022
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa61801 |
| first_indexed |
2022-11-28T15:41:25Z |
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| last_indexed |
2023-01-13T19:22:48Z |
| id |
cronfa61801 |
| recordtype |
SURis |
| fullrecord |
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| spelling |
2022-11-28T15:41:26.9942332 v2 61801 2022-11-07 A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2022-11-07 ACEM Journal Article IEEE Transactions on Intelligent Transportation Systems 23 11 22156 22165 Institute of Electrical and Electronics Engineers (IEEE) 1524-9050 1558-0016 1 11 2022 2022-11-01 10.1109/tits.2022.3160524 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University International Research Cooperation Seed Fund of Beijing University of Technology (Grant Number: 2021A05); National Natural Science Foundation of China (Grant Number: 52008012); alent Promotion Program by Beijing Association for Science and Technology; Construction of Service Capability of Scientific and Technological Innovation-Municipal Level of Fundamental Research Funds (Scientific Research Categories), Beijing; Fundamental Research Funds from BJUT 2022-11-28T15:41:26.9942332 2022-11-07T19:28:02.6505098 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yue Hou 0000-0002-4334-2620 1 Shuo Liu 2 Dandan Cao 0000-0002-4277-5942 3 Bo Peng 0000-0002-6932-0622 4 Zhuo Liu 0000-0001-9356-8989 5 Wenjuan Sun 0000-0003-0546-2389 6 Ning Chen 7 |
| title |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| spellingShingle |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images Yue Hou |
| title_short |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| title_full |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| title_fullStr |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| title_full_unstemmed |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| title_sort |
A Deep Learning Method for Pavement Crack Identification Based on Limited Field Images |
| author_id_str_mv |
92bf566c65343cb3ee04ad963eacf31b |
| author_id_fullname_str_mv |
92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou |
| author |
Yue Hou |
| author2 |
Yue Hou Shuo Liu Dandan Cao Bo Peng Zhuo Liu Wenjuan Sun Ning Chen |
| format |
Journal article |
| container_title |
IEEE Transactions on Intelligent Transportation Systems |
| container_volume |
23 |
| container_issue |
11 |
| container_start_page |
22156 |
| publishDate |
2022 |
| institution |
Swansea University |
| issn |
1524-9050 1558-0016 |
| doi_str_mv |
10.1109/tits.2022.3160524 |
| publisher |
Institute of Electrical and Electronics Engineers (IEEE) |
| college_str |
Faculty of Science and Engineering |
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|
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facultyofscienceandengineering |
| hierarchy_top_title |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
| hierarchy_parent_title |
Faculty of Science and Engineering |
| department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering |
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0 |
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| published_date |
2022-11-01T17:15:28Z |
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1850689379509469184 |
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11.08899 |

