Encoder–Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation
The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art...
Gespeichert in:
| Veröffentlicht in: | Applied sciences Jg. 13; H. 17; S. 9924 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Basel
MDPI AG
01.09.2023
|
| Schlagworte: | |
| ISSN: | 2076-3417, 2076-3417 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self–calibrated RGB-D image semantic segmentation neural network model based on an improved residual network without relying on depth sensors, which utilizes multi-modal information from depth maps predicted with depth estimation models and RGB image fusion for image semantic segmentation to enhance the understanding of a scene. First, we designed a novel convolution neural network (CNN) with an encoding and decoding structure as our semantic segmentation model. The encoder was constructed using IResNet to extract the semantic features of the RGB image and the predicted depth map and then effectively fuse them with the self–calibration fusion structure. The decoder restored the resolution of the output features with a series of successive upsampling structures. Second, we presented a feature pyramid attention mechanism to extract the fused information at multiple scales and obtain features with rich semantic information. The experimental results using the publicly available Cityscapes dataset and collected forest scene images show that our model trained with the estimated depth information can achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task and even outperforming some competitive methods. |
|---|---|
| AbstractList | The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although RGB–D images can provide additional depth information for improving the performance of semantic segmentation tasks, current state–of–the–art methods directly use ground truth depth maps for depth information fusion, which relies on highly developed and expensive depth sensors. Aiming to solve such a problem, we proposed a self–calibrated RGB-D image semantic segmentation neural network model based on an improved residual network without relying on depth sensors, which utilizes multi-modal information from depth maps predicted with depth estimation models and RGB image fusion for image semantic segmentation to enhance the understanding of a scene. First, we designed a novel convolution neural network (CNN) with an encoding and decoding structure as our semantic segmentation model. The encoder was constructed using IResNet to extract the semantic features of the RGB image and the predicted depth map and then effectively fuse them with the self–calibration fusion structure. The decoder restored the resolution of the output features with a series of successive upsampling structures. Second, we presented a feature pyramid attention mechanism to extract the fused information at multiple scales and obtain features with rich semantic information. The experimental results using the publicly available Cityscapes dataset and collected forest scene images show that our model trained with the estimated depth information can achieve comparable performance to the ground truth depth map in improving the accuracy of the semantic segmentation task and even outperforming some competitive methods. |
| Audience | Academic |
| Author | Chen, Songnan Kan, Jiangming Dong, Ruifang Tang, Mengxia |
| Author_xml | – sequence: 1 givenname: Songnan orcidid: 0000-0003-0314-1194 surname: Chen fullname: Chen, Songnan – sequence: 2 givenname: Mengxia surname: Tang fullname: Tang, Mengxia – sequence: 3 givenname: Ruifang surname: Dong fullname: Dong, Ruifang – sequence: 4 givenname: Jiangming surname: Kan fullname: Kan, Jiangming |
| BookMark | eNptUctuFDEQtFCQCCEnfmAkjmiDXzMeH6M8YKVIOQS4Wh67vXi1Yw8ez4Fb_oE_5Evo7IAUIexDl7qrSv14TU5STkDIW0YvhND0g50mJpjSmssX5JRT1W2EZOrkGX5Fzud5T_FpJnpGT8nXm-Syh_Lr8ec1HFHzUMvi6lKguV3mmHbNNUz1W7NNIZfR1phTg6i5X6rPGB9gtKlGh2A3QqpHxhvyMtjDDOd_4hn5cnvz-erT5u7-4_bq8m7jJBV1I8Er5ZVslQahBqXo0HOPtT4wOVjX69B76y2WleJMd25wotPeatWqAbg4I9vV12e7N1OJoy0_TLbRHBO57Iwt2NwBzODdYG0QAlomAzgtAx06J9sAwUrWote71Wsq-fsCczX7vJSE7Rved5xzyqlG1sXK2lk0jbiUWqzD72GMDi8SIuYvVSd5J1vxJGCrwJU8zwWCcXFdEgrjwTBqns5nnp0PNe__0fwd7X_s36KInvE |
| CitedBy_id | crossref_primary_10_1007_s11042_024_19051_9 crossref_primary_10_3390_f15010194 |
| Cites_doi | 10.1109/TITS.2017.2750080 10.1109/CVPR.2016.90 10.1016/j.neucom.2021.01.126 10.1109/34.868688 10.1109/LRA.2020.3007457 10.1109/CVPR.2017.660 10.3390/rs14132976 10.1109/TPAMI.2016.2644615 10.1177/01423312211062972 10.1109/TSMC.1979.4310076 10.1007/978-3-031-20056-4_2 10.1016/j.asoc.2020.106804 10.1016/j.compbiomed.2022.106231 10.1109/ICIP.2019.8803025 10.1109/CVPR.2019.01289 10.1109/TPAMI.2021.3132068 10.1109/TPAMI.2017.2699184 10.3390/electronics8030331 10.1007/s42979-021-00592-x 10.1109/CVPR.2017.189 10.1016/j.neucom.2023.126469 10.1109/ICIP.2019.8803360 10.1109/TPAMI.2008.132 10.1109/TPAMI.2015.2505283 10.1109/CVPR.2016.350 10.1109/TETCI.2022.3160720 10.1109/CVPR.2017.549 10.1109/ACCESS.2021.3055497 10.1109/ICCV.2015.123 10.1016/j.neucom.2023.03.006 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2023 MDPI AG 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: COPYRIGHT 2023 MDPI AG – notice: 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | AAYXX CITATION ABUWG AFKRA AZQEC BENPR CCPQU DWQXO PHGZM PHGZT PIMPY PKEHL PQEST PQQKQ PQUKI DOA |
| DOI | 10.3390/app13179924 |
| DatabaseName | CrossRef ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central ProQuest One Community College ProQuest Central ProQuest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Academic (retired) ProQuest One Academic UKI Edition DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Publicly Available Content Database ProQuest Central ProQuest One Academic Middle East (New) ProQuest One Academic UKI Edition ProQuest Central Essentials ProQuest Central Korea ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) ProQuest One Community College ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) |
| DatabaseTitleList | CrossRef Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: DOA name: Directory of Open Access Journals (DOAJ) url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Sciences (General) |
| EISSN | 2076-3417 |
| ExternalDocumentID | oai_doaj_org_article_bdcbaaf33e514fec94f0b6c45fefa415 A764264539 10_3390_app13179924 |
| GroupedDBID | .4S 2XV 5VS 7XC 8CJ 8FE 8FG 8FH AADQD AAFWJ AAYXX ADBBV ADMLS AFFHD AFKRA AFPKN AFZYC ALMA_UNASSIGNED_HOLDINGS APEBS ARCSS BCNDV BENPR CCPQU CITATION CZ9 D1I D1J D1K GROUPED_DOAJ IAO IGS ITC K6- K6V KC. KQ8 L6V LK5 LK8 M7R MODMG M~E OK1 P62 PHGZM PHGZT PIMPY PROAC TUS ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI |
| ID | FETCH-LOGICAL-c403t-4ed77d74579e37b770b82dc408f14bac89f8dada79e772196cbc369da9757be23 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 1 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001060500200001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2076-3417 |
| IngestDate | Tue Oct 14 18:47:22 EDT 2025 Sun Nov 09 08:14:05 EST 2025 Tue Nov 04 18:36:49 EST 2025 Sat Nov 29 07:11:15 EST 2025 Tue Nov 18 21:58:17 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 17 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c403t-4ed77d74579e37b770b82dc408f14bac89f8dada79e772196cbc369da9757be23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-0314-1194 |
| OpenAccessLink | https://doaj.org/article/bdcbaaf33e514fec94f0b6c45fefa415 |
| PQID | 2862220209 |
| PQPubID | 2032433 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_bdcbaaf33e514fec94f0b6c45fefa415 proquest_journals_2862220209 gale_infotracacademiconefile_A764264539 crossref_citationtrail_10_3390_app13179924 crossref_primary_10_3390_app13179924 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-09-01 |
| PublicationDateYYYYMMDD | 2023-09-01 |
| PublicationDate_xml | – month: 09 year: 2023 text: 2023-09-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Basel |
| PublicationPlace_xml | – name: Basel |
| PublicationTitle | Applied sciences |
| PublicationYear | 2023 |
| Publisher | MDPI AG |
| Publisher_xml | – name: MDPI AG |
| References | Srivastava (ref_35) 2014; 15 Sun (ref_16) 2020; 5 Li (ref_22) 2021; 48 ref_36 ref_13 Li (ref_14) 2021; 44 ref_12 ref_34 ref_33 ref_31 ref_30 Cong (ref_6) 2016; 103 ref_19 Badrinarayanan (ref_32) 2017; 39 ref_17 ref_38 Zhou (ref_18) 2023; 7 ref_37 Chen (ref_39) 2018; 40 Tang (ref_25) 2021; 9 Liu (ref_21) 2016; 38 Long (ref_11) 2015; 39 Sarker (ref_10) 2021; 2 Yi (ref_4) 2022; 151 Awad (ref_8) 2022; 5 ref_24 Chen (ref_26) 2020; 97 Shi (ref_7) 2000; 22 ref_20 Otsu (ref_5) 1979; 9 ref_40 ref_1 He (ref_42) 2021; 440 Ge (ref_23) 2023; 550 ref_29 ref_28 ref_27 Karri (ref_3) 2022; 151 ref_9 Lin (ref_15) 2023; 535 Saxena (ref_43) 2009; 31 Romera (ref_41) 2017; 19 Fusic (ref_2) 2022; 44 |
| References_xml | – ident: ref_28 – volume: 19 start-page: 263 year: 2017 ident: ref_41 article-title: Erfnet: Efficient residual factorized convnet for real–time semantic segmentation publication-title: IEEE Trans. Intell. Transp. Syst. doi: 10.1109/TITS.2017.2750080 – ident: ref_24 – ident: ref_29 doi: 10.1109/CVPR.2016.90 – volume: 440 start-page: 251 year: 2021 ident: ref_42 article-title: SOSD–Net: Joint semantic object segmentation and depth estimation from monocular images publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.01.126 – volume: 103 start-page: 3505 year: 2016 ident: ref_6 article-title: Application of Watershed Algorithm for Segmenting Overlapping Cells in Microscopic Image publication-title: J. Image Graph. – volume: 22 start-page: 888 year: 2000 ident: ref_7 article-title: Normalized cuts and image segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/34.868688 – volume: 5 start-page: 5558 year: 2020 ident: ref_16 article-title: Real–Time Fusion Network for RGB–D Semantic Segmentation Incorporating Unexpected Obstacle Detection for Road–Driving Images publication-title: IEEE Robot. Autom. Lett. doi: 10.1109/LRA.2020.3007457 – volume: 5 start-page: 141 year: 2022 ident: ref_8 article-title: Evaluation of Nonparametric Machine–Learning Algorithms for an Optimal Crop Classification Using Big Data Reduction Strategy publication-title: Remote Sens. Earth Syst. Sci. – ident: ref_33 doi: 10.1109/CVPR.2017.660 – ident: ref_9 doi: 10.3390/rs14132976 – volume: 39 start-page: 640 year: 2015 ident: ref_11 article-title: Fully Convolutional Networks for Semantic Segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – volume: 39 start-page: 2481 year: 2017 ident: ref_32 article-title: SegNet: A Deep Convolutional Encoder–Decoder Architecture for Image Segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2016.2644615 – ident: ref_40 – volume: 44 start-page: 2574 year: 2022 ident: ref_2 article-title: Scene terrain classification for autonomous vehicle navigation based on semantic segmentation method publication-title: Trans. Inst. Meas. Control doi: 10.1177/01423312211062972 – volume: 9 start-page: 62 year: 1979 ident: ref_5 article-title: A threshold selection method from gray–level histograms publication-title: IEEE Trans. Syst. Man Cybern. doi: 10.1109/TSMC.1979.4310076 – ident: ref_19 doi: 10.1007/978-3-031-20056-4_2 – volume: 97 start-page: 106804 year: 2020 ident: ref_26 article-title: Monocular Image Depth Prediction without Depth Sensors: An Unsupervised Learning Method publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2020.106804 – ident: ref_37 – volume: 151 start-page: 106231 year: 2022 ident: ref_3 article-title: Explainable multi–module semantic guided attention based network for medical image segmentation publication-title: Comput. Biol. Med. doi: 10.1016/j.compbiomed.2022.106231 – ident: ref_17 doi: 10.1109/ICIP.2019.8803025 – ident: ref_38 doi: 10.1109/CVPR.2019.01289 – volume: 44 start-page: 9904 year: 2021 ident: ref_14 article-title: CTNet: Context–Based Tandem Network for Semantic Segmentation publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2021.3132068 – volume: 40 start-page: 834 year: 2018 ident: ref_39 article-title: DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2017.2699184 – ident: ref_1 doi: 10.3390/electronics8030331 – volume: 15 start-page: 1929 year: 2014 ident: ref_35 article-title: Dropout: A Simple Way to Prevent Neural Networks from Overfitting publication-title: J. Mach. Learn. Res. – volume: 2 start-page: 160 year: 2021 ident: ref_10 article-title: Machine Learning: Algorithms, Real–World Applications and Research Directions publication-title: SN Comput. Sci. doi: 10.1007/s42979-021-00592-x – ident: ref_34 doi: 10.1109/CVPR.2017.189 – volume: 550 start-page: 126469 year: 2023 ident: ref_23 article-title: Unsupervised domain adaptation via style adaptation and boundary enhancement for medical semantic segmentation publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.126469 – ident: ref_12 – ident: ref_20 doi: 10.1109/ICIP.2019.8803360 – volume: 31 start-page: 824 year: 2009 ident: ref_43 article-title: Make3D: Learning 3D Scene Structure from a Single Still Image publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2008.132 – volume: 38 start-page: 2024 year: 2016 ident: ref_21 article-title: Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2505283 – ident: ref_27 doi: 10.1109/CVPR.2016.350 – volume: 7 start-page: 598 year: 2023 ident: ref_18 article-title: RFNet: Reverse Fusion Network with Attention Mechanism for RGB–D Indoor Scene Understanding publication-title: IEEE Trans. Emerg. Top. Comput. Intell. doi: 10.1109/TETCI.2022.3160720 – ident: ref_31 doi: 10.1109/CVPR.2017.549 – ident: ref_13 – volume: 9 start-page: 22640 year: 2021 ident: ref_25 article-title: Encoder–Decoder Structure with the Feature Pyramid for Depth Estimation from a Single Image publication-title: IEEE Access doi: 10.1109/ACCESS.2021.3055497 – ident: ref_36 – ident: ref_30 doi: 10.1109/ICCV.2015.123 – volume: 48 start-page: 200069 year: 2021 ident: ref_22 article-title: RGB–D object recognition algorithm based on improved double stream convolution recursive neural network publication-title: Opto–Electron. Eng. – volume: 535 start-page: 53 year: 2023 ident: ref_15 article-title: Multi–stage context refinement network for semantic segmentation publication-title: Neurocomputing doi: 10.1016/j.neucom.2023.03.006 – volume: 151 start-page: 106231 year: 2022 ident: ref_4 article-title: CCTseg: A cascade composite transformer semantic segmentation network for UAV visual perception publication-title: Measurement |
| SSID | ssj0000913810 |
| Score | 2.2801592 |
| Snippet | The semantic segmentation of outdoor images is the cornerstone of scene understanding and plays a crucial role in the autonomous navigation of robots. Although... |
| SourceID | doaj proquest gale crossref |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database |
| StartPage | 9924 |
| SubjectTerms | Accuracy Artificial intelligence Deep learning feature pyramid fusion structure Interdisciplinary subjects Machine learning Neural networks predicted depth map RGB–D image Robots semantic segmentation Semantics Sensors |
| SummonAdditionalLinks | – databaseName: Publicly Available Content Database dbid: PIMPY link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwELZgy4EegBZQFwrKAYkfKWpiO7F9qgrtCiQoKxWqcorssb1Uotlld8uZd-AN-yTMJN5SJODEKVbsg5NvPJ6xZ75h7AnoiNtYCTm3kucylHWupZQ5OhvcRldY39WMPH6rDg_1yYkZp_ToRQqrXOnETlH3bM8Ut41KeMdPgU7Mdzga4hz99sLszr7mVEOK7lpTQY3rbI2It_SArY3fvBt_ujxzIQ5MXRZ9mp5Ab59uiUtBpGhc_rYxdfz9f9PS3dYzuv1_J32H3UomaLbXy8wGuxbaTbZ-hZhwk22kJb_IniVe6ud32fFBSwnw84vvP_ZD18qOOvbZ83nIRhRAP8n2w2z5OUs5ToR5hq3s_fnST_F5FM4QyFPAxuQsJT2199jH0cGHV6_zVJYhB1mIJQLqlfJKVsoEoZxShdPcY5-OpXQWtInaW2-xG013XOHgQNTGW6Mq5QIX99mgnbZhi2Wu1BE4aACp0FML2lW11AYi_ikhlB2yFytMGkic5VQ640uDvgsB2FwBcIiStxo866k6_jzsJYF7OYT4tbsX0_mkScu1cR6ctVGIgAZlDGBkLFwNsoohWrR5huwpiUZDWgAnBDYlM-BnEZ9Ws6dqMjUrYYZseyUaTVIPi-aXJDz4d_dDdpPq2_dBbdtsgKiGR-wGfFueLuaPk3z_BIdVDbo priority: 102 providerName: ProQuest |
| Title | Encoder–Decoder Structure Fusing Depth Information for Outdoor Semantic Segmentation |
| URI | https://www.proquest.com/docview/2862220209 https://doaj.org/article/bdcbaaf33e514fec94f0b6c45fefa415 |
| Volume | 13 |
| WOSCitedRecordID | wos001060500200001&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: PRVAON databaseName: Directory of Open Access Journals (DOAJ) customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: DOA dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: M~E dateStart: 20110101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: ProQuest Central (ProQuest) customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2076-3417 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000913810 issn: 2076-3417 databaseCode: PIMPY dateStart: 20110101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1Lb9QwELZQy6EcKlqKWFoqH5CAShEb24ntYx-7AgmWFX2oPVn22G4r0bTa3XLuf-Af8ksYJ95qKxVx4RQntiJ7Hp4Z2fMNIW9BRTRjJRTMClaIUNaFEkIUGGwwG13f-rZm5MkXORqp01M9Xij1le6EdfDAHeE-Og_O2sh5QNMeA2gR-64GUcUQbZdeztDrWQim2j1Ylwm6qkvI4xjXp_Pgkif4MyYemKAWqf9v-3FrZIbPyWr2DuluN6s18iQ06-TZAmbgOlnL2jil7zNk9IcX5GTQpNz0ye-7XwehbdHDFhj2dhLoMN1tP6cH4WZ2QXP6UWIHxRb9djvz1_g8DFdI40vAxvlVzkdqNsjxcHC0_6nIFRMKEH0-Q1p7Kb0UldSBSydl3ynmsU_FUjgLSkflrbfYjV41Kh844LX2VstKusD4S7LUXDfhFaGuVBEYKAAhMYgKylW1UBoihkCcS9sjO3MiGshw4qmqxQ-DYUWiuFmgeA-FYj74pkPReHzYXuLG_ZAEfd1-QIEwWSDMvwSiR94lXpqkoDghsDnPAJeVoK7MrqyTF1hx3SNbc3abrLlTw3B9jKETrV__j9lskhX8mUjWrqy2yBLyPrwhT-Hn7HI62SbLe4PR-Pt2K7z4Nv78dXz2B8l9-Eo |
| linkProvider | Directory of Open Access Journals |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3NbhMxELaqFAk4AC0gAgX2AOJHWrFre9f2AaFCGjVqGiK1VOVk_Bsq0U1IUhA33oH34KF4Esa73lIk4NYDp7VsH-ydz-MZ2_MNQg8M97CN5SbFiuKUurxMOaU0BWcDK68zZeuckQdDNhrxw0MxXkHf21iY8Kyy1Ym1orZTE87In2EwvTF46pl4MfuYhqxR4Xa1TaHRwGLHffkMLtvi-aAH8n2IcX9r_9V2GrMKpIZmZAnjsYxZRgsmHGGasUxzbKGN-5xqZbjw3CqroBksTwCo0YaUwirBCqZdIDoAlb9KAey8g1bHg93x29NTncCyyfOsCQQkRGThHjongXYN09-2vjpDwN_2gXpz61_9337LNXQlmtHJZoP7NbTiqnV0-Qy54jpai2prkTyO3NpPrqODrSoE8c9_fP3Wc3Up2asZdE_mLumHIIBJ0nOz5fskxmkF3CZQSl6fLO0UvnvuGMB4ZKAwOY6BW9UN9OZcZnsTdapp5W6hROfcG2y4MZSBt-m4LkrKhfEgGUKY6qKnrdSlibzrIf3HBwn-V4CIPAORLqyetvOsoRv5c7eXAT6nXQJHeF0xnU9kVDlSW6OV8oQ4MIq9M4L6TJeGFt55BXZbFz0K4JNBk8GAjIoBGTCtwAkmN1kZzOWCiC7aaMEno4pbyF_Iu_3v5vvo4vb-7lAOB6OdO-gS1JHmkd4G6oCE3V10wXxaHi3m9-JqStC780bqT1nDX-c |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V3LbhMxFLWqFCFYAC0gAgW8APGQRp2xPbG9QKiQRkQtIVKhKivjZ6hEJyFJQez4B_6Gz-FLuJ7xlCIBuy5YjWV7YY-P78P2PRehe1YEUGOFzYhmJGO-6GWCMZaBs0F0MLl2dc7I_V0-GomDAzleQd_bWJj4rLKVibWgdlMbz8g3CZjeBDz1XG6G9Cxi3B88nX3MYgapeNPaptNoILLjv3wG923xZNiHtb5PyGD79fMXWcowkFmW0yWMzXHuOCu59JQbznMjiIM2EQpmtBUyCKedhmawQgGs1ljak05LXnLjI-kBiP9VMMkZ66DV8fDl-O3JCU9k3BRF3gQFUirzeCdd0EjBRthvarDOFvA3nVArusHl__kXXUGXknmNt5r9sIZWfLWOLp4iXVxHa0mcLfDDxLn96Cra365icP_8x9dvfV-X8F7NrHs893gQgwMmuO9ny_c4xW9FPGMo4VfHSzeF754_ApAeWihMjlJAV3UNvTmT2V5HnWpa-RsIm0IES6ywFtAhSy9M2WNC2gCrRCnXXfS4RYCyiY89pgX5oMAvi3BRp-DShV3Vdp41NCR_7vYsQumkS-QOryum84lKokgZZ43WgVIPxnLwVrKQm55lZfBBgz3XRQ8iEFWUcDAgq1OgBkwrcoWpLd6LZnRJZRdttEBUSfQt1C8U3vx38110HuCpdoejnVvoAlTR5u3eBurAAvvb6Jz9tDxczO-kjYXRu7MG6k9szmio |
| 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=Encoder%E2%80%93Decoder+Structure+Fusing+Depth+Information+for+Outdoor+Semantic+Segmentation&rft.jtitle=Applied+sciences&rft.au=Chen%2C+Songnan&rft.au=Tang%2C+Mengxia&rft.au=Dong%2C+Ruifang&rft.au=Kan%2C+Jiangming&rft.date=2023-09-01&rft.pub=MDPI+AG&rft.issn=2076-3417&rft.eissn=2076-3417&rft.volume=13&rft.issue=17&rft_id=info:doi/10.3390%2Fapp13179924&rft.externalDocID=A764264539 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2076-3417&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2076-3417&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2076-3417&client=summon |