No Cover Image

Journal article 402 views 131 downloads

Advanced low‐light image transformation for accurate nighttime pavement distress detection

Yuanyuan Hu, Hancheng Zhang, Yue Hou Orcid Logo, Pengfei Liu

Computer‐Aided Civil and Infrastructure Engineering

Swansea University Author: Yue Hou Orcid Logo

  • mice.70001.pdf

    PDF | Version of Record

    © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.

    Download (18.2MB)

Check full text

DOI (Published version): 10.1111/mice.70001

Abstract

Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet,...

Full description

Published in: Computer‐Aided Civil and Infrastructure Engineering
ISSN: 1093-9687 1467-8667
Published: Wiley 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa69826
first_indexed 2025-06-26T14:06:54Z
last_indexed 2025-10-01T10:15:46Z
id cronfa69826
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-09-30T15:45:55.8876095</datestamp><bib-version>v2</bib-version><id>69826</id><entry>2025-06-26</entry><title>Advanced low&#x2010;light image transformation for accurate nighttime pavement distress detection</title><swanseaauthors><author><sid>92bf566c65343cb3ee04ad963eacf31b</sid><ORCID>0000-0002-4334-2620</ORCID><firstname>Yue</firstname><surname>Hou</surname><name>Yue Hou</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-06-26</date><deptcode>ACEM</deptcode><abstract>Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet, a novel model that transforms low&#x2010;light images into high&#x2010;quality, daylight&#x2010;like representations for pavement distress detection. By employing unpaired image translation techniques, aligned nighttime&#x2013;daytime datasets are generated for supervised training. The model integrates a lightEnhance generator, multiscale feature discriminators, and distress&#x2010;focused loss function, ensuring accurate reconstruction of critical pavement details. Experimental results show that IllumiShiftNet achieves a state&#x2010;of&#x2010;the&#x2010;art peak signal&#x2010;to&#x2010;noise ratio of 28.5 and a structural similarity index measure of 0.78, enabling detection algorithms trained on daytime data to perform effectively on nighttime imagery. The model demonstrates robust performance across varying illuminance levels, adverse weather conditions, and diverse road types while maintaining real&#x2010;time processing capabilities. These results establish IllumiShiftNet as a practical solution for nighttime pavement monitoring.</abstract><type>Journal Article</type><journal>Computer&#x2010;Aided Civil and Infrastructure Engineering</journal><volume>0</volume><journalNumber/><paginationStart/><paginationEnd/><publisher>Wiley</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>1093-9687</issnPrint><issnElectronic>1467-8667</issnElectronic><keywords/><publishedDay>25</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-06-25</publishedDate><doi>10.1111/mice.70001</doi><url/><notes/><college>COLLEGE NANME</college><department>Aerospace, Civil, Electrical, and Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>ACEM</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>This paper is funded by the German Research Foundation (DFG) under LI 3613/5-2 with Project ID 414936990. Open access funding enabled and organized by Projekt DEAL.</funders><projectreference/><lastEdited>2025-09-30T15:45:55.8876095</lastEdited><Created>2025-06-26T15:01:58.0986933</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering</level></path><authors><author><firstname>Yuanyuan</firstname><surname>Hu</surname><order>1</order></author><author><firstname>Hancheng</firstname><surname>Zhang</surname><order>2</order></author><author><firstname>Yue</firstname><surname>Hou</surname><orcid>0000-0002-4334-2620</orcid><order>3</order></author><author><firstname>Pengfei</firstname><surname>Liu</surname><order>4</order></author></authors><documents><document><filename>69826__34598__3bd37ab25f1f4287a223883c45b9f71b.pdf</filename><originalFilename>mice.70001.pdf</originalFilename><uploaded>2025-06-26T15:01:58.0728560</uploaded><type>Output</type><contentLength>19081258</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-09-30T15:45:55.8876095 v2 69826 2025-06-26 Advanced low‐light image transformation for accurate nighttime pavement distress detection 92bf566c65343cb3ee04ad963eacf31b 0000-0002-4334-2620 Yue Hou Yue Hou true false 2025-06-26 ACEM Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet, a novel model that transforms low‐light images into high‐quality, daylight‐like representations for pavement distress detection. By employing unpaired image translation techniques, aligned nighttime–daytime datasets are generated for supervised training. The model integrates a lightEnhance generator, multiscale feature discriminators, and distress‐focused loss function, ensuring accurate reconstruction of critical pavement details. Experimental results show that IllumiShiftNet achieves a state‐of‐the‐art peak signal‐to‐noise ratio of 28.5 and a structural similarity index measure of 0.78, enabling detection algorithms trained on daytime data to perform effectively on nighttime imagery. The model demonstrates robust performance across varying illuminance levels, adverse weather conditions, and diverse road types while maintaining real‐time processing capabilities. These results establish IllumiShiftNet as a practical solution for nighttime pavement monitoring. Journal Article Computer‐Aided Civil and Infrastructure Engineering 0 Wiley 1093-9687 1467-8667 25 6 2025 2025-06-25 10.1111/mice.70001 COLLEGE NANME Aerospace, Civil, Electrical, and Mechanical Engineering COLLEGE CODE ACEM Swansea University Another institution paid the OA fee This paper is funded by the German Research Foundation (DFG) under LI 3613/5-2 with Project ID 414936990. Open access funding enabled and organized by Projekt DEAL. 2025-09-30T15:45:55.8876095 2025-06-26T15:01:58.0986933 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Civil Engineering Yuanyuan Hu 1 Hancheng Zhang 2 Yue Hou 0000-0002-4334-2620 3 Pengfei Liu 4 69826__34598__3bd37ab25f1f4287a223883c45b9f71b.pdf mice.70001.pdf 2025-06-26T15:01:58.0728560 Output 19081258 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Advanced low‐light image transformation for accurate nighttime pavement distress detection
spellingShingle Advanced low‐light image transformation for accurate nighttime pavement distress detection
Yue Hou
title_short Advanced low‐light image transformation for accurate nighttime pavement distress detection
title_full Advanced low‐light image transformation for accurate nighttime pavement distress detection
title_fullStr Advanced low‐light image transformation for accurate nighttime pavement distress detection
title_full_unstemmed Advanced low‐light image transformation for accurate nighttime pavement distress detection
title_sort Advanced low‐light image transformation for accurate nighttime pavement distress detection
author_id_str_mv 92bf566c65343cb3ee04ad963eacf31b
author_id_fullname_str_mv 92bf566c65343cb3ee04ad963eacf31b_***_Yue Hou
author Yue Hou
author2 Yuanyuan Hu
Hancheng Zhang
Yue Hou
Pengfei Liu
format Journal article
container_title Computer‐Aided Civil and Infrastructure Engineering
container_volume 0
publishDate 2025
institution Swansea University
issn 1093-9687
1467-8667
doi_str_mv 10.1111/mice.70001
publisher Wiley
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id 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
document_store_str 1
active_str 0
description Pavement distress detection is critical for road safety and infrastructure longevity. Although nighttime inspections offer advantages such as reduced traffic and enhanced operational efficiency, challenges like low visibility and noise hinder their effectiveness. This paper presents IllumiShiftNet, a novel model that transforms low‐light images into high‐quality, daylight‐like representations for pavement distress detection. By employing unpaired image translation techniques, aligned nighttime–daytime datasets are generated for supervised training. The model integrates a lightEnhance generator, multiscale feature discriminators, and distress‐focused loss function, ensuring accurate reconstruction of critical pavement details. Experimental results show that IllumiShiftNet achieves a state‐of‐the‐art peak signal‐to‐noise ratio of 28.5 and a structural similarity index measure of 0.78, enabling detection algorithms trained on daytime data to perform effectively on nighttime imagery. The model demonstrates robust performance across varying illuminance levels, adverse weather conditions, and diverse road types while maintaining real‐time processing capabilities. These results establish IllumiShiftNet as a practical solution for nighttime pavement monitoring.
published_date 2025-06-25T07:44:07Z
_version_ 1850744029933731840
score 11.088929

Similar Items