A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level
As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network...
Uložené v:
| Vydané v: | Computer-aided civil and infrastructure engineering Ročník 37; číslo 13; s. 1721 - 1736 |
|---|---|
| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hoboken
Wiley Subscription Services, Inc
01.11.2022
|
| Predmet: | |
| ISSN: | 1093-9687, 1467-8667 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding network and attention mechanism and can thus extract the crack pixels more accurately and efficiently. The proposed network achieves an excellent detection performance with IOU = 92.85%, precision = 96.90%, recall = 95.36%, F1 = 95.53%. Compared with the other advanced networks, the accuracy of the proposed method is substantially enhanced. The quantitative estimation of key geometrical features of cracks including length, width, and area is successfully realized with the development of a prototype of an intelligent mobile system. Compared with the ground truth, the maximum crack width shows the lowest relative error rate, which ranges −31.75%∼28.57%. |
|---|---|
| AbstractList | As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding network and attention mechanism and can thus extract the crack pixels more accurately and efficiently. The proposed network achieves an excellent detection performance with IOU = 92.85%, precision = 96.90%, recall = 95.36%, F1 = 95.53%. Compared with the other advanced networks, the accuracy of the proposed method is substantially enhanced. The quantitative estimation of key geometrical features of cracks including length, width, and area is successfully realized with the development of a prototype of an intelligent mobile system. Compared with the ground truth, the maximum crack width shows the lowest relative error rate, which ranges −31.75%∼28.57%. As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and quantification of crack geometry are thus critical for the decision‐making of maintenance measures. This paper proposes a novel neural network for the detection and evaluation of road cracks at pixel level, which combines the advantage of encoding–decoding network and attention mechanism and can thus extract the crack pixels more accurately and efficiently. The proposed network achieves an excellent detection performance with IOU = 92.85%, precision = 96.90%, recall = 95.36%, F1 = 95.53%. Compared with the other advanced networks, the accuracy of the proposed method is substantially enhanced. The quantitative estimation of key geometrical features of cracks including length, width, and area is successfully realized with the development of a prototype of an intelligent mobile system. Compared with the ground truth, the maximum crack width shows the lowest relative error rate, which ranges −31.75%∼28.57%. |
| Author | Chen, Jun He, Ye |
| Author_xml | – sequence: 1 givenname: Jun surname: Chen fullname: Chen, Jun email: junchen@buaa.edu.cn organization: Beihang University – sequence: 2 givenname: Ye surname: He fullname: He, Ye organization: Beihang University |
| BookMark | eNp9kM9OAjEQxhuDiYBefIIm3kwW292l7R4J8Q8JxoueN6U7GypLi20BvfEIJr4hT2JZPBnjXGY6-X3zpV8PdYw1gNAlJQMa62apFQxoKlJ2gro0ZzwRjPFOnEmRJQUT_Az1vH8lsfI866LdCBu7gQa_7Heffi5XUGEwylbg9ruvCtoJGwhb6xZ4q8McyxDABG0NXoKaS6P9EtfW4QoCqHYvTTyykc1atk9bY2dlhZWTauGjHq_0e7RsIBqfo9NaNh4ufnofvdzdPo8fkunT_WQ8miYqI5Qlac6HOaW1YNlQ5UIKAYwrRQSvgNCiqikQDgpkXXCo6mzGOMzkEGZFwUkq86yPro53V86-rcGH8tWunYmWZcpTRjJacBEpcqSUs947qEulQ_uL4KRuSkrKQ87lIeeyzTlKrn9JVk4vpfv4G6ZHeKsb-PiHLB8n49uj5huS95Rc |
| CitedBy_id | crossref_primary_10_1016_j_autcon_2023_105186 crossref_primary_10_1111_mice_13200 crossref_primary_10_1080_01616412_2025_2527899 crossref_primary_10_1080_10298436_2023_2246097 crossref_primary_10_1111_mice_13241 crossref_primary_10_1109_TITS_2024_3384018 crossref_primary_10_1111_mice_13128 crossref_primary_10_1007_s11760_025_04362_7 crossref_primary_10_1111_mice_13446 crossref_primary_10_1016_j_aei_2025_103610 crossref_primary_10_1111_mice_13489 crossref_primary_10_1111_mice_13444 crossref_primary_10_3390_s24010003 crossref_primary_10_1109_TASE_2025_3565647 crossref_primary_10_1111_mice_13003 crossref_primary_10_1111_mice_70041 crossref_primary_10_1007_s42452_024_06207_3 crossref_primary_10_1016_j_engappai_2025_110784 crossref_primary_10_1109_TITS_2024_3467257 crossref_primary_10_1016_j_ymssp_2024_111131 crossref_primary_10_1111_mice_12909 crossref_primary_10_1061_JCCEE5_CPENG_5868 crossref_primary_10_1108_ECAM_07_2023_0705 crossref_primary_10_1111_mice_13132 crossref_primary_10_1080_10298436_2024_2402838 crossref_primary_10_1016_j_autcon_2024_105797 crossref_primary_10_1155_2023_9940881 crossref_primary_10_3390_app14051892 crossref_primary_10_1016_j_dsp_2024_104598 crossref_primary_10_1111_mice_13014 crossref_primary_10_1111_mice_12918 crossref_primary_10_1038_s44172_025_00431_4 crossref_primary_10_1016_j_eswa_2023_121686 crossref_primary_10_1155_2023_9982080 crossref_primary_10_3233_ICA_240734 crossref_primary_10_1007_s11709_024_1048_4 crossref_primary_10_1016_j_eswa_2024_123314 crossref_primary_10_1111_mice_13063 crossref_primary_10_1016_j_jobe_2024_111440 crossref_primary_10_1111_mice_12932 crossref_primary_10_1111_mice_13467 crossref_primary_10_1016_j_autcon_2023_104950 crossref_primary_10_1016_j_autcon_2024_105322 crossref_primary_10_1111_mice_13103 crossref_primary_10_1061_JCCEE5_CPENG_6621 crossref_primary_10_3390_app13127227 crossref_primary_10_1155_2024_5532909 crossref_primary_10_1016_j_engstruct_2023_116988 crossref_primary_10_1016_j_aei_2024_102498 crossref_primary_10_1007_s42947_025_00573_w crossref_primary_10_1111_mice_70021 crossref_primary_10_1016_j_measurement_2024_116595 crossref_primary_10_3390_s24175586 crossref_primary_10_1007_s11440_023_01889_2 crossref_primary_10_1038_s41598_025_91352_x crossref_primary_10_1016_j_autcon_2024_105331 crossref_primary_10_1111_mice_13233 crossref_primary_10_1186_s40537_025_01065_1 crossref_primary_10_1111_mice_13110 crossref_primary_10_1111_mice_13231 crossref_primary_10_1016_j_neucom_2025_131485 crossref_primary_10_1111_mice_12987 crossref_primary_10_1111_mice_13437 crossref_primary_10_1111_mice_13117 crossref_primary_10_1177_14727978251321985 crossref_primary_10_1111_mice_12984 crossref_primary_10_1111_mice_13477 crossref_primary_10_1109_TITS_2025_3558279 crossref_primary_10_1016_j_aei_2024_102586 crossref_primary_10_1111_mice_70050 crossref_primary_10_1111_mice_13071 crossref_primary_10_3390_e26040328 |
| Cites_doi | 10.1109/ICIP.2016.7533052 10.1111/mice.12519 10.1109/RCAE51546.2020.9294343 10.1016/j.autcon.2018.11.028 10.1109/TSMC.1979.4310076 10.1111/mice.12141 10.1061/(ASCE)CO.1943-7862.0001570 10.1111/exsy.12647 10.1080/15732479.2011.593891 10.14359/51689560 10.1109/ISPA.2019.8868619 10.1111/mice.12622 10.1002/ecj.10151 10.1061/9780784413623.088 10.1016/j.autcon.2019.04.005 10.1111/mice.12550 10.1111/mice.12334 10.1111/mice.12564 10.3233/ICA-2010-0345 10.1007/978-3-319-24574-4_28 10.1109/CVPR.2019.00065 10.1111/mice.12433 10.1007/s00521-019-04146-4 10.1016/S0146-664X(77)80024-5 10.1109/TNNLS.2017.2682102 10.1002/tal.1400 10.3390/s20030717 10.1111/mice.12297 10.1111/mice.12532 10.1111/mice.12440 10.1061/(ASCE)CO.1943-7862.0001047 10.1111/j.1467-8667.2011.00716.x 10.1111/mice.12263 10.1109/TPAMI.1986.4767851 10.1111/mice.12387 10.1159/000512985 10.3390/app10228089 10.1016/j.autcon.2020.103176 10.1016/j.soildyn.2017.05.013 10.1111/mice.12412 10.1007/s00521-019-04359-7 10.1177/0361198120907283 10.1016/j.autcon.2020.103357 10.1145/357994.358023 |
| ContentType | Journal Article |
| Copyright | 2022 . 2022 Computer‐Aided Civil and Infrastructure Engineering. |
| Copyright_xml | – notice: 2022 . – notice: 2022 Computer‐Aided Civil and Infrastructure Engineering. |
| DBID | AAYXX CITATION 7SC 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DOI | 10.1111/mice.12826 |
| DatabaseName | CrossRef Computer and Information Systems Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Civil Engineering Abstracts Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional |
| DatabaseTitle | CrossRef Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | CrossRef Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Applied Sciences Engineering Computer Science |
| EISSN | 1467-8667 |
| EndPage | 1736 |
| ExternalDocumentID | 10_1111_mice_12826 MICE12826 |
| Genre | article |
| GroupedDBID | ..I .3N .4S .DC .GA 05W 0R~ 10A 1OB 1OC 29F 33P 3SF 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5HH 5LA 5VS 66C 6P2 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHHS AAHQN AAMNL AANLZ AAONW AAXRX AAYCA AAZKR ABCQN ABCUV ABDBF ABFSI ABJNI ACAHQ ACCFJ ACCZN ACGFS ACPOU ACUHS ACXBN ACXQS ADBBV ADEOM ADIZJ ADKYN ADMGS ADOZA ADXAS ADZMN ADZOD AEEZP AEIGN AEIMD AENEX AEQDE AEUQT AEUYR AFBPY AFEBI AFFPM AFGKR AFPWT AHBTC AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ARCSS ATUGU AUFTA AZBYB AZVAB BAFTC BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DPXWK DR2 DRFUL DRSTM DU5 EAD EAP EBS EDO EMK EST ESX F00 F01 F04 G-S G.N GODZA H.T H.X HGLYW HZI HZ~ I-F IHE IX1 J0M K48 LATKE LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LYRES MEWTI MK4 MK~ MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 NF~ O66 O9- OIG P2P P2W P2X P4D Q.N Q11 QB0 R.K RX1 SUPJJ TN5 TUS UB1 W8V W99 WBKPD WIH WIK WLBEL WOHZO WQJ WRC WXSBR WYISQ XG1 ZZTAW ~IA ~WT 31~ AAMMB AANHP AASGY AAYXX ABEML ACBWZ ACRPL ACSCC ACYXJ ADMLS ADNMO AEFGJ AEYWJ AGHNM AGQPQ AGXDD AGYGG AHEFC AI. AIDQK AIDYY AIQQE ASPBG AVWKF AZFZN BDRZF CAG CITATION COF CWDTD E.L EJD FEDTE HF~ HVGLF LW6 O8X PALCI RJQFR SAMSI VH1 7SC 8FD FR3 JQ2 KR7 L7M L~C L~D |
| ID | FETCH-LOGICAL-c3016-2475411f8635c48a88e67cc087de019df1e07eceaf97edf3b67eba5eb99702a43 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 79 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000757245200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1093-9687 |
| IngestDate | Sun Nov 09 07:15:27 EST 2025 Tue Nov 18 22:42:18 EST 2025 Sat Nov 29 05:42:09 EST 2025 Wed Jan 22 16:23:30 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 13 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c3016-2475411f8635c48a88e67cc087de019df1e07eceaf97edf3b67eba5eb99702a43 |
| Notes | Funding information National Key Research and Development Program of China, Grant/Award Number: 2018YFB1600200; National Natural Science Foundation of China, Grant/Award Number: 51978027. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 2726031978 |
| PQPubID | 2045171 |
| PageCount | 16 |
| ParticipantIDs | proquest_journals_2726031978 crossref_citationtrail_10_1111_mice_12826 crossref_primary_10_1111_mice_12826 wiley_primary_10_1111_mice_12826_MICE12826 |
| PublicationCentury | 2000 |
| PublicationDate | 1 November 2022 2022-11-00 20221101 |
| PublicationDateYYYYMMDD | 2022-11-01 |
| PublicationDate_xml | – month: 11 year: 2022 text: 1 November 2022 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Hoboken |
| PublicationPlace_xml | – name: Hoboken |
| PublicationTitle | Computer-aided civil and infrastructure engineering |
| PublicationYear | 2022 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2015; 37 2018; 144 2020; 20 2017b; 100 2019; 99 1984; 27 2010; 17 2020; 83 2019; 34 2015; 30 2019; 104 1998 2020; 37 2020; 35 2020; 32 2020; 10 2017c; 26 2017; 114 2016; 142 2013; 9 2021; 36 2009; 92 2018; 1801.05746 2020 2018; 11(4) 2017; 32 2019 2020; 27 2017 2016 2015 2012; 27 2014 2020; 114 1986; PAMI‐8 2018; 33 2020; 2674 2020; 119 2017a; 28 1979; 9 1977; 6 e_1_2_8_28_1 Oktay O. (e_1_2_8_32_1) 2018; 11 e_1_2_8_24_1 e_1_2_8_47_1 e_1_2_8_26_1 e_1_2_8_49_1 Simonyan K. (e_1_2_8_44_1) 2014 e_1_2_8_3_1 e_1_2_8_5_1 e_1_2_8_7_1 e_1_2_8_9_1 Kelvin X. (e_1_2_8_20_1) 2015; 37 e_1_2_8_43_1 e_1_2_8_22_1 e_1_2_8_41_1 e_1_2_8_17_1 e_1_2_8_13_1 e_1_2_8_36_1 e_1_2_8_15_1 e_1_2_8_38_1 e_1_2_8_11_1 e_1_2_8_34_1 e_1_2_8_53_1 Tillotson H. (e_1_2_8_45_1) 1998 e_1_2_8_51_1 e_1_2_8_30_1 e_1_2_8_29_1 Ju H. (e_1_2_8_19_1) 2020; 27 e_1_2_8_25_1 e_1_2_8_46_1 e_1_2_8_27_1 e_1_2_8_48_1 e_1_2_8_2_1 e_1_2_8_4_1 e_1_2_8_6_1 e_1_2_8_8_1 e_1_2_8_21_1 e_1_2_8_42_1 Iglovikov V. (e_1_2_8_14_1) 2018; 1801 e_1_2_8_23_1 Goodfellow B. Y. I. (e_1_2_8_12_1) 2016 e_1_2_8_40_1 e_1_2_8_18_1 e_1_2_8_39_1 e_1_2_8_35_1 e_1_2_8_16_1 e_1_2_8_37_1 e_1_2_8_10_1 e_1_2_8_31_1 e_1_2_8_33_1 e_1_2_8_54_1 e_1_2_8_52_1 e_1_2_8_50_1 |
| References_xml | – volume: 37 start-page: 2048 year: 2015 end-page: 2057 article-title: Show, attend and tell: Neural image caption generation with visual attention publication-title: Computer Science – volume: 33 start-page: 731 issue: 9 year: 2018 end-page: 747 article-title: Autonomous structural visual inspection using region‐based deep learning for detecting multiple damage types publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 36 start-page: 61 issue: 1 year: 2021 end-page: 72 article-title: Automatic detection method of cracks from concrete surface imagery using two‐step Light Gradient Boosting Machine publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 1801.05746 year: 2018 article-title: TernausNet: U‐Net with VGG11 encoder pre‐trained on ImageNet for image segmentation publication-title: ArXiv – volume: 142 issue: 2 year: 2016 article-title: A novel machine learning model for estimation of sale prices of real estate units publication-title: Construction Engineering and Management – volume: 36 start-page: 14 issue: 1 year: 2021 end-page: 29 article-title: Automated crack evaluation of a bridge pier using a climbing robot publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 100 start-page: 417 year: 2017b end-page: 427 article-title: NEEWS: A novel earthquake early warning system using neural dynamic classification and neural dynamic optimization model publication-title: Soil Dynamics and Earthquake Engineering – volume: 17 start-page: 197 issue: 3 year: 2010 end-page: 210 article-title: Enhanced probabilistic neural network with local decision circles: A robust classifier publication-title: Integrated Computer‐Aided Engineering – start-page: 558 year: 2019 end-page: 567 article-title: Bag of tricks for image classification with convolutional neural networks – volume: 6 start-page: 492 issue: 5 year: 1977 end-page: 501 article-title: Edge detection by compass gradient masks publication-title: Computer Graphics and Image Processing – volume: 11(4) start-page: 1 year: 2018 end-page: 10 article-title: Attention U‐Net: Learning where to look for the pancreas publication-title: ArXiv, 1804.03999 – start-page: 5998 year: 2017 end-page: 6008 article-title: Attention is all you need – start-page: 11 year: 1998 end-page: 23 article-title: Detecting cracks by image analysis on publication-title: Computing in Civil Engineering – volume: 104 start-page: 129 year: 2019 end-page: 139 article-title: Computer vision‐based concrete crack detection using U‐Net fully convolutional networks publication-title: Automation in Construction – volume: 9 start-page: 567 issue: 6 year: 2013 end-page: 577 article-title: Automated image processing technique for detecting and analysing concrete surface cracks publication-title: Structure and Infrastructure Engineering – year: 2014 article-title: Very deep convolutional networks for large‐scale image recognition publication-title: Computer Science – start-page: 103 year: 2020 end-page: 107 article-title: Crack detecting by recursive attention U‐Net – start-page: 234 year: 2015 end-page: 241 article-title: U‐Net: Convolutional networks for biomedical image segmentation – year: 2015 – volume: 114 start-page: 237 issue: 2 year: 2017 end-page: 244 article-title: Supervised deep restricted boltzmann machine for estimation of concrete compressive strength publication-title: ACI Materials Journal – volume: 32 start-page: 805 issue: 10 year: 2017 end-page: 819 article-title: Automated pixel‐level pavement crack detection on 3D asphalt surfaces using a deep‐learning network publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 10 start-page: 8089 issue: 8089 year: 2020 article-title: Improved photoacoustic imaging of numerical bone model based on attention block U‐Net deep learning network publication-title: Applied Sciences – volume: 32 start-page: 6393 issue: 10 year: 2020 end-page: 6404 article-title: FEMa: A finite element machine for fast learning publication-title: Neural Computing and Applications – volume: 114 year: 2020 article-title: An integrated approach to automatic pixel‐level crack detection and quantification of asphalt pavement publication-title: Automation in Construction – volume: 92 start-page: 1 issue: 10 year: 2009 end-page: 12 article-title: Practical image measurement of crack width for real concrete structure publication-title: Electronics and Communications in Japan – volume: 30 start-page: 759 issue: 10 year: 2015 end-page: 770 article-title: Vision‐based automated crack detection for bridge inspection publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: PAMI‐8 start-page: 679 issue: 6 year: 1986 end-page: 698 article-title: A computational approach to edge detection publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence – volume: 9 start-page: 62 issue: 1 year: 1979 end-page: 66 article-title: A threshold selection method from gray level histograms publication-title: IEEE Transactions on Systems, Man, and Cybernetics – year: 2019 article-title: Automatic crack detection using mask R‐CNN – volume: 27 start-page: 236 year: 1984 end-page: 239 article-title: A fast parallel algorithm for thinning digital patterns publication-title: Communications of the ACM – volume: 37 issue: 6 year: 2020 article-title: Deep learning techniques for recommender systems based on collaborative filtering publication-title: Expert Systems – volume: 28 start-page: 3074 issue: 12 year: 2017a end-page: 3083 article-title: A new neural dynamic classification algorithm publication-title: IEEE Transactions on Neural Networks and Learning Systems – volume: 83 start-page: 602 issue: 6 year: 2020 end-page: 614 article-title: Detection of epileptic seizure using pre‐trained deep convolutional neural network and transfer learning publication-title: European Neurology – volume: 144 start-page: 2018 issue: 12 year: 2018 article-title: A novel machine learning model for construction cost estimation taking into account economic variables and indices publication-title: Journal of Construction Engineering and Management – volume: 2674 start-page: 328 issue: 2 year: 2020 end-page: 339 article-title: Pavement image datasets: A new benchmark dataset to classify and densify pavement distresses publication-title: Transportation Research Record – volume: 33 start-page: 1127 issue: 2 year: 2018 end-page: 1141 article-title: Road damage detection and classification using deep neural networks with smartphone images: Road damage detection and classification publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 34 start-page: 713 issue: 8 year: 2019 end-page: 727 article-title: Encoder‐decoder network for pixel‐level road crack detection in black‐box images publication-title: Computer Aided Civil Infrastructure Engineering – volume: 20 start-page: 717 issue: 3 year: 2020 article-title: Automatic tunnel crack detection based on U‐Net and a convolutional neural network with alternately updated clique publication-title: Sensors – year: 2016 – volume: 33 start-page: 1090 issue: 12 year: 2018 end-page: 1109 article-title: Automatic pixel‐level crack detection and measurement using fully convolutional network publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 35 start-page: 549 issue: 6 year: 2020 end-page: 564 article-title: Real‐time crack assessment using deep neural networks with wall‐climbing unmanned aerial system publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 26 issue: 18 year: 2017c article-title: A novel machine learning based algorithm to detect damage in highrise building structures publication-title: The Structural Design of Tall and Special Buildings – volume: 119 year: 2020 article-title: Aspatial‐channel hierarchical deep learning network for pixel‐level automated crack detection publication-title: Automation in Construction – volume: 32 start-page: 361 issue: 5 year: 2017 end-page: 378 article-title: Deep learning‐based crack damage detection using convolutional neural networks publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 35 start-page: 775 issue: 8 year: 2020 end-page: 792 article-title: Applicability of machine learning to a crack model in concrete bridges publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 34 start-page: 616 issue: 7 year: 2019 end-page: 634 article-title: Automatic pixel‐level multiple damage detection of concrete structure using fully convolutional network publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 27 issue: 5 year: 2020 article-title: CrackU‐Net: A novel deep convolutional neural network for pixelwise pavement crack detection publication-title: Structural Control and Health Monitoring – volume: 99 start-page: 52 year: 2019 end-page: 58 article-title: Autonomous concrete crack detection using deep fully convolutional neural network publication-title: Automation in Construction – volume: 27 start-page: 29 issue: 1 year: 2012 end-page: 47 article-title: Concrete crack detection by multiple sequential image filtering publication-title: Computer‐Aided Civil and Infrastructure Engineering – volume: 35 start-page: 1291 issue: 11 year: 2020 end-page: 1305 article-title: Automated pavement crack detection and segmentation based on two‐step convolutional neural network publication-title: Computer‐Aided Civil and Infrastructure Engineering – start-page: 919 year: 2014 end-page: 924 – volume: 32 start-page: 8675 issue: 10 year: 2020 end-page: 8690 article-title: A dynamic ensemble learning algorithm for neural networks publication-title: Neural Computing with Applications – start-page: 3708 year: 2016 end-page: 3712 article-title: Road crack detection using deep convolutional neural network – volume: 1801 year: 2018 ident: e_1_2_8_14_1 article-title: TernausNet: U‐Net with VGG11 encoder pre‐trained on ImageNet for image segmentation publication-title: ArXiv – ident: e_1_2_8_53_1 doi: 10.1109/ICIP.2016.7533052 – ident: e_1_2_8_18_1 doi: 10.1111/mice.12519 – ident: e_1_2_8_47_1 doi: 10.1109/RCAE51546.2020.9294343 – ident: e_1_2_8_11_1 doi: 10.1016/j.autcon.2018.11.028 – ident: e_1_2_8_33_1 doi: 10.1109/TSMC.1979.4310076 – ident: e_1_2_8_51_1 doi: 10.1111/mice.12141 – ident: e_1_2_8_40_1 doi: 10.1061/(ASCE)CO.1943-7862.0001570 – ident: e_1_2_8_28_1 doi: 10.1111/exsy.12647 – ident: e_1_2_8_21_1 doi: 10.1080/15732479.2011.593891 – ident: e_1_2_8_41_1 doi: 10.14359/51689560 – ident: e_1_2_8_4_1 doi: 10.1109/ISPA.2019.8868619 – ident: e_1_2_8_24_1 doi: 10.1111/mice.12622 – ident: e_1_2_8_48_1 doi: 10.1002/ecj.10151 – volume: 27 start-page: e2551 issue: 5 year: 2020 ident: e_1_2_8_19_1 article-title: CrackU‐Net: A novel deep convolutional neural network for pixelwise pavement crack detection publication-title: Structural Control and Health Monitoring – ident: e_1_2_8_50_1 doi: 10.1061/9780784413623.088 – ident: e_1_2_8_25_1 doi: 10.1016/j.autcon.2019.04.005 – ident: e_1_2_8_16_1 doi: 10.1111/mice.12550 – ident: e_1_2_8_15_1 – ident: e_1_2_8_8_1 doi: 10.1111/mice.12334 – ident: e_1_2_8_10_1 doi: 10.1111/mice.12564 – ident: e_1_2_8_2_1 doi: 10.3233/ICA-2010-0345 – ident: e_1_2_8_43_1 doi: 10.1007/978-3-319-24574-4_28 – year: 2014 ident: e_1_2_8_44_1 article-title: Very deep convolutional networks for large‐scale image recognition publication-title: Computer Science – ident: e_1_2_8_13_1 doi: 10.1109/CVPR.2019.00065 – ident: e_1_2_8_23_1 doi: 10.1111/mice.12433 – ident: e_1_2_8_35_1 doi: 10.1007/s00521-019-04146-4 – volume: 11 start-page: 1 year: 2018 ident: e_1_2_8_32_1 article-title: Attention U‐Net: Learning where to look for the pancreas publication-title: ArXiv, 1804.03999 – ident: e_1_2_8_42_1 doi: 10.1016/S0146-664X(77)80024-5 – ident: e_1_2_8_37_1 doi: 10.1109/TNNLS.2017.2682102 – ident: e_1_2_8_39_1 doi: 10.1002/tal.1400 – ident: e_1_2_8_22_1 doi: 10.3390/s20030717 – ident: e_1_2_8_52_1 doi: 10.1111/mice.12297 – ident: e_1_2_8_31_1 doi: 10.1111/mice.12532 – ident: e_1_2_8_5_1 doi: 10.1111/mice.12440 – start-page: 11 year: 1998 ident: e_1_2_8_45_1 article-title: Detecting cracks by image analysis on a Parallel Computer publication-title: Computing in Civil Engineering – ident: e_1_2_8_36_1 doi: 10.1061/(ASCE)CO.1943-7862.0001047 – ident: e_1_2_8_29_1 doi: 10.1111/j.1467-8667.2011.00716.x – ident: e_1_2_8_7_1 doi: 10.1111/mice.12263 – volume: 37 start-page: 2048 year: 2015 ident: e_1_2_8_20_1 article-title: Show, attend and tell: Neural image caption generation with visual attention publication-title: Computer Science – ident: e_1_2_8_6_1 doi: 10.1109/TPAMI.1986.4767851 – ident: e_1_2_8_26_1 doi: 10.1111/mice.12387 – ident: e_1_2_8_30_1 doi: 10.1159/000512985 – ident: e_1_2_8_9_1 doi: 10.3390/app10228089 – ident: e_1_2_8_17_1 doi: 10.1016/j.autcon.2020.103176 – ident: e_1_2_8_38_1 doi: 10.1016/j.soildyn.2017.05.013 – ident: e_1_2_8_46_1 – ident: e_1_2_8_49_1 doi: 10.1111/mice.12412 – volume-title: Deep learning year: 2016 ident: e_1_2_8_12_1 – ident: e_1_2_8_3_1 doi: 10.1007/s00521-019-04359-7 – ident: e_1_2_8_27_1 doi: 10.1177/0361198120907283 – ident: e_1_2_8_34_1 doi: 10.1016/j.autcon.2020.103357 – ident: e_1_2_8_54_1 doi: 10.1145/357994.358023 |
| SSID | ssj0000443 |
| Score | 2.5733259 |
| Snippet | As the most common road distress, cracks have a substantial influence on the integrity of pavement structures. Accurate identification of crack existence and... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1721 |
| SubjectTerms | Coders Crack geometry Cracks Decoding Flaw detection Neural networks Pavement condition Pixels |
| Title | A novel U‐shaped encoder–decoder network with attention mechanism for detection and evaluation of road cracks at pixel level |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1111%2Fmice.12826 https://www.proquest.com/docview/2726031978 |
| Volume | 37 |
| WOSCitedRecordID | wos000757245200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1467-8667 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000443 issn: 1093-9687 databaseCode: DRFUL dateStart: 19970101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NahsxEB5Sp4f2kL821G0SBO2lhS1reS1pIZfQxuQQQigx5LZopRE1OGuz64Ye_QiFvmGepCNZa7tQCiG3XSFpF2l-PomZbwA-lHpAfkn5-i7cJNmA66Q0ziTkm0VuyOOW1oZiE_LqSt3e5tdbcNrmwiz5IVYXbl4zgr32Cq7LZkPJfbX2z2RduXgG25wEN-vA9tdvw9Hl2hJnMcA-7ye5UDLSk_pInvXovx3SGmVuYtXgbIa7T_vNPdiJIJOdLaViH7awOoDdCDhZVOeGmtqaDm3bAbzcICh8BYszVk3vccJGD4tfzXc9o-Ge-dJi_bD4bTE8sWoZSs78nS7zfJ0hgpLdoc8qHjd3jIAxszgPUV8V0xVNsiIZZ1PH6qm2zNQ-3Z_Gs9n4J31y4sOZXsNoeH7z5SKJNRsSQ6ZCJDyTg6zXc4qAjMmUVgqFNCZV0iKhSet6mEo0qF0u0bp-KSSSvGCZ5zLlOusfQqeaVvgGmCh1z7mUo3AEe0quHKaC3jzBD2I66MLHduMKEwnNfV2NSdEebPzaF2Htu_B-1Xe2pPH4Z6-jdv-LqMpNwSX3lbjptN2FT2Gn_zNDQbpzHp7ePqbzO3jBfVpFyHE8gs68_oHH8Nzcz8dNfRLF-g-Iyf83 |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fa9swED-2dLDtod26jWbtNsH2soGLo9iW_Fi2ho5lYYwG-mZk6cQCqRPstOwxH2Gwb9hPspMiJymUwdibLSTZSPfnJ3H3O4B3pUrJL0lX34XrKEm5ikptdUS-Ocs1edzSGF9sQoxG8uIi_xZic1wuzIofYn3h5jTD22un4O5CekvLXbn2YzKvPLsPOwnJUdqBnU_fB-PhxhQnIcI-70d5JkXgJ3WhPJvRtz3SBmZug1XvbQZ7__mfT2A3wEx2spKLp3APq33YC5CTBYVuqKmt6tC27cPjLYrCZ7A8YdXsGqdsfLP81fxQcxruuC8N1jfL3wb9E6tWweTM3eoyx9jpYyjZJbq84klzyQgaM4MLH_dVMVXRJGuacTazrJ4pw3TtEv5pPJtPftInpy6g6TmMB6fnH8-iULUh0mQssognIk16PSsJyuhEKikxE1rHUhgkPGlsD2OBGpXNBRrbLzOBJDFY5rmIuUr6L6BTzSo8AJaVqmdtzDGzBHxKLi3GGb05ih_EOO3C-3bnCh0ozV1ljWnRHm3c2hd-7bvwdt13viLyuLPXUSsARVDmpuCCu1rcdN7uwge_1X-ZoSDtOfVPL_-l8xt4eHb-dVgMP4--HMIj7pIsfMbjEXQW9RW-ggf6ejFp6tdBxv8AwpEDNg |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1faxQxEB-0FdGHVmuLp1UD-qKwspfbTbKPpe2hWI4iHvRtySYTPLjuHbtn8fE-guA37CdxksvenSCC-LYbkuySzJ9fwsxvAN5UOie_pHx9F26SLOc6qYwzCflmURjyuJW1odiEHI3U1VVxGWNzfC7Mih9ifeHmNSPYa6_gOLduS8t9ufb3ZF65uAu7WV4I0svds8_D8cXGFGcxwr4YJIVQMvKT-lCezejfPdIGZm6D1eBthvv_-Z-PYC_CTHaykovHcAfrA9iPkJNFhW6pqavq0LUdwMMtisInsDxh9ewGp2x8u_zRftVzGu65Ly02t8ufFsMTq1fB5Mzf6jLP2BliKNk1-rziSXvNCBozi4sQ91UzXdMka5pxNnOsmWnLTOMT_mk8m0--0yenPqDpEMbD8y-nH5JYtSExZCxEwjOZZ_2-UwRlTKa0UiikMamSFglPWtfHVKJB7QqJ1g0qIZEkBquikCnX2eAIdupZjU-BiUr3nUs5CkfAp-LKYSrozVP8IKZ5D952O1eaSGnuK2tMy-5o49e-DGvfg9frvvMVkccfex13AlBGZW5LLrmvxU3n7R68C1v9lxlK0p7z8PTsXzq_gvuXZ8Py4uPo03N4wH2ORUh4PIadRfMNX8A9c7OYtM3LKOK_AEqsArE |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=A+novel+U%E2%80%90shaped+encoder%E2%80%93decoder+network+with+attention+mechanism+for+detection+and+evaluation+of+road+cracks+at+pixel+level&rft.jtitle=Computer-aided+civil+and+infrastructure+engineering&rft.au=Chen%2C+Jun&rft.au=He%2C+Ye&rft.date=2022-11-01&rft.pub=Wiley+Subscription+Services%2C+Inc&rft.issn=1093-9687&rft.eissn=1467-8667&rft.volume=37&rft.issue=13&rft.spage=1721&rft.epage=1736&rft_id=info:doi/10.1111%2Fmice.12826&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1093-9687&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1093-9687&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1093-9687&client=summon |