Vehicle-pedestrian Instance Segmentation Algorithm Based on Improved YOLOv8n-seg
In the field of autonomous driving, the rapid and precise perception of the environment, along with effective segmentation of vehicles and pedestrians, is a critical area of research. The complexity of real-world scenes, often characterized by occlusions, can lead to suboptimal segmentation results....
Uložené v:
| Vydané v: | Engineering letters Ročník 33; číslo 6; s. 1879 |
|---|---|
| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Hong Kong
International Association of Engineers
01.06.2025
|
| Predmet: | |
| ISSN: | 1816-093X, 1816-0948 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | In the field of autonomous driving, the rapid and precise perception of the environment, along with effective segmentation of vehicles and pedestrians, is a critical area of research. The complexity of real-world scenes, often characterized by occlusions, can lead to suboptimal segmentation results. Moreover, the computational demands of existing models require substantial resources and time for processing image data. In autonomous driving systems, timely perception and decision-making are essential; computational delays can hinder vehicle responsiveness and increase the risk of driving errors. To enhance the performance of vehicle-pedestrian segmentation, this paper proposes a novel single-stage instance segmentation approach based on an improved YOLOv8n-seg model. This improvement involves redesigning the bottleneck module in the core C2f module of YOLOv8n-seg and replacing the feature fusion layer with a Bidirectional Feature Pyramid Network (BiFPN) structure. Evaluations conducted on the Cityscapes dataset demonstrate that our method achieves a 3.1% increase in the mAP50-95mask value and a 1% reduction in FLOPs compared to the original YOLOv8n-seg. Furthermore, experiments on the COCO subset show that our approach achieves a mAP50mask of 55.6%, mAP50-95mask of 34.2%, and significantly improves segmentation performance under various real-world conditions. Consequently, our approach not only enhances segmentation accuracy but also reduces computational complexity, effectively meeting the real-time requirements for vehicle segmentation in autonomous driving applications. |
|---|---|
| AbstractList | In the field of autonomous driving, the rapid and precise perception of the environment, along with effective segmentation of vehicles and pedestrians, is a critical area of research. The complexity of real-world scenes, often characterized by occlusions, can lead to suboptimal segmentation results. Moreover, the computational demands of existing models require substantial resources and time for processing image data. In autonomous driving systems, timely perception and decision-making are essential; computational delays can hinder vehicle responsiveness and increase the risk of driving errors. To enhance the performance of vehicle-pedestrian segmentation, this paper proposes a novel single-stage instance segmentation approach based on an improved YOLOv8n-seg model. This improvement involves redesigning the bottleneck module in the core C2f module of YOLOv8n-seg and replacing the feature fusion layer with a Bidirectional Feature Pyramid Network (BiFPN) structure. Evaluations conducted on the Cityscapes dataset demonstrate that our method achieves a 3.1% increase in the mAP50-95mask value and a 1% reduction in FLOPs compared to the original YOLOv8n-seg. Furthermore, experiments on the COCO subset show that our approach achieves a mAP50mask of 55.6%, mAP50-95mask of 34.2%, and significantly improves segmentation performance under various real-world conditions. Consequently, our approach not only enhances segmentation accuracy but also reduces computational complexity, effectively meeting the real-time requirements for vehicle segmentation in autonomous driving applications. |
| Author | Zhang, Xinhe Su, Bochao Zhu, Wenxuan Fang, Siwen |
| Author_xml | – sequence: 1 givenname: Siwen surname: Fang fullname: Fang, Siwen – sequence: 2 givenname: Xinhe surname: Zhang fullname: Zhang, Xinhe – sequence: 3 givenname: Bochao surname: Su fullname: Su, Bochao – sequence: 4 givenname: Wenxuan surname: Zhu fullname: Zhu, Wenxuan |
| BookMark | eNo9jstqwzAUREVJoWmafzB0bZAlWb5apqGPgMGFPmhXQY8bx8WWXUvJ91fQ0tUczmJmrsnCjx4vyLKAQuZUCVj8M_-4IusQOkOFqHipaLkkz-947GyP-YQOQ5w77bOdD1F7i9kLtgP6qGM3-mzTt-PcxeOQ3emALktqN0zzeE782dTNGXwesL0hlwfdB1z_5Yq8Pdy_bp_yunncbTd1PhVQxhwQuKPOVBwUMwpkoSsNlTTKGI4HQOTWCFtKgUlQZ4UzDJKRjAJaxlfk9rc3Xfg-pev7r_E0-zS554xxKaSCiv8AMTVPYg |
| ContentType | Journal Article |
| Copyright | Copyright International Association of Engineers 2025 |
| Copyright_xml | – notice: Copyright International Association of Engineers 2025 |
| DBID | 7SC 7TB 8FD FR3 JQ2 KR7 L7M L~C L~D |
| DatabaseName | Computer and Information Systems Abstracts Mechanical & Transportation Engineering 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 | Civil Engineering Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Civil Engineering Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1816-0948 |
| GroupedDBID | 29G 2WC 5GY 5VS 7SC 7TB 8FD AAKPC ABDBF ACIWK ACUHS ADMLS ALMA_UNASSIGNED_HOLDINGS EOJEC ESX FR3 I-F JQ2 KQ8 KR7 L7M L~C L~D MK~ OBODZ OK1 OVT P2P TR2 TUS ~8M |
| ID | FETCH-LOGICAL-p185t-8e83d0db73892b9861a7a876b9bb3ef8ee3cb4c564ebb30dc4db28b4c6208ec23 |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001506721900013&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1816-093X |
| IngestDate | Mon Jun 30 07:22:44 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-p185t-8e83d0db73892b9861a7a876b9bb3ef8ee3cb4c564ebb30dc4db28b4c6208ec23 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| PQID | 3223646987 |
| PQPubID | 2049041 |
| ParticipantIDs | proquest_journals_3223646987 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-06-01 |
| PublicationDateYYYYMMDD | 2025-06-01 |
| PublicationDate_xml | – month: 06 year: 2025 text: 2025-06-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Hong Kong |
| PublicationPlace_xml | – name: Hong Kong |
| PublicationTitle | Engineering letters |
| PublicationYear | 2025 |
| Publisher | International Association of Engineers |
| Publisher_xml | – name: International Association of Engineers |
| SSID | ssib044735905 ssj0000314636 |
| Score | 2.3161726 |
| Snippet | In the field of autonomous driving, the rapid and precise perception of the environment, along with effective segmentation of vehicles and pedestrians, is a... |
| SourceID | proquest |
| SourceType | Aggregation Database |
| StartPage | 1879 |
| SubjectTerms | Complexity Instance segmentation Modules Pedestrians Perception Performance enhancement Real time |
| Title | Vehicle-pedestrian Instance Segmentation Algorithm Based on Improved YOLOv8n-seg |
| URI | https://www.proquest.com/docview/3223646987 |
| Volume | 33 |
| WOSCitedRecordID | wos001506721900013&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: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1816-0948 dateEnd: 99991231 omitProxy: false ssIdentifier: ssib044735905 issn: 1816-093X databaseCode: M~E dateStart: 20060101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV07b9swECaaoEM7BOkLTZMWGoouBgGZlERyTAMHHVw7QJzUnQxRPD-AhHb9SD3lt-dIUZaCAEU7dCGI0wOEvhPveLzjR8hnaVTKjVFUpyBoIvKcasg4ZUyCdnm6sS8fu-6KXk8Oh-oipBWtPJ2AsFZut2rxX6FGGYLtSmf_Ae7dS1GAfQQdW4Qd278C_hqmTkQXYMCTclifEeBLAy5hchuKjWzr9GYyX87W09vWVzRlxm0blCEG7P_sd_t30tIVTB7F7uvTC1s3vhCoLh-pAs-z33V12S4cPZzZ6U6FLjdeq-bFNJ_Xd3rhD7DbTVDYEItgaZ0zVa5PHwUxG_rlsyTDCBsxSPQu8HnlCYHRGDVl5fGb1RRdnpURVLE53zqu9NqSVbv3vf7o_KrbHQ06w8GXxS_qOMbcXnwgXNkjezx2PAvf7zvV3JM4CmYV5j5vxXnbnaXm1uvVKJ-Ya--DDA7JQVg8RKcl6K_IM7CvycsGKG_IxVP4owr-qAl_tIM_8vBHKKrgjxrwvyVX553B2TcaWDPoAn2vNZUguYmNFuiKMq1k1s5FjjZPK605jCUAL3RSpFkCKIhNkRjNJEoyFksoGH9H9u3cwnsSpZJnwuCamokxut0yN7zN9DiLpYFUFckROak-yCj8AasRWgieOV5S8eHPl4_Ji1qHTsj-ermBj-R5cbeerZafPDoPUQxdsg |
| linkProvider | ISSN International Centre |
| 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=Vehicle-pedestrian+Instance+Segmentation+Algorithm+Based+on+Improved+YOLOv8n-seg&rft.jtitle=Engineering+letters&rft.au=Fang%2C+Siwen&rft.au=Zhang%2C+Xinhe&rft.au=Su%2C+Bochao&rft.au=Zhu%2C+Wenxuan&rft.date=2025-06-01&rft.pub=International+Association+of+Engineers&rft.issn=1816-093X&rft.eissn=1816-0948&rft.volume=33&rft.issue=6&rft.spage=1879&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1816-093X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1816-093X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1816-093X&client=summon |