Enhanced YOLOv8 for Drilling Core Image Segmentation
The exploration of deep formations by drilling has brought the issue of rock fragment into focus, emphasizing the need for accurate assessment of the fragment state. Core sampling, involving the extraction of cylindrical rock samples, has become a widely used method for evaluating formation properti...
Uloženo v:
| Vydáno v: | Chinese Control Conference s. 7774 - 7779 |
|---|---|
| Hlavní autoři: | , , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
Technical Committee on Control Theory, Chinese Association of Automation
28.07.2025
|
| Témata: | |
| ISSN: | 1934-1768 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | The exploration of deep formations by drilling has brought the issue of rock fragment into focus, emphasizing the need for accurate assessment of the fragment state. Core sampling, involving the extraction of cylindrical rock samples, has become a widely used method for evaluating formation properties, including fragmentation. However, traditional core analysis is labor-intensive and challenges arise in accurately assessing fine fractured particles. Recent advancements in deep learning, particularly convolutional neural networks (CNN), have shown promise in automating the estimation of Rock Quality Designation (RQD), a key measure of rock mass quality. Despite these advancements, variations in core sizes and dense target distributions in core images pose significant challenges for segmentation. Existing research has focused primarily on larger intact cores, leaving a gap in the evaluation of smaller fractured rock fragments. This paper investigates the application of advanced segmentation techniques, including an enhanced version of YOLOv8, for core and fracture segmentation. The proposed model effectively addresses challenges associated with fine fractured particles and multi-scale feature extraction, improving segmentation accuracy and reliability. Results demonstrate that the enhanced YOLOv8 model outperforms traditional methods in extracting core fragments, offering a more scalable and efficient solution for fracture assessment in drilling operations. |
|---|---|
| AbstractList | The exploration of deep formations by drilling has brought the issue of rock fragment into focus, emphasizing the need for accurate assessment of the fragment state. Core sampling, involving the extraction of cylindrical rock samples, has become a widely used method for evaluating formation properties, including fragmentation. However, traditional core analysis is labor-intensive and challenges arise in accurately assessing fine fractured particles. Recent advancements in deep learning, particularly convolutional neural networks (CNN), have shown promise in automating the estimation of Rock Quality Designation (RQD), a key measure of rock mass quality. Despite these advancements, variations in core sizes and dense target distributions in core images pose significant challenges for segmentation. Existing research has focused primarily on larger intact cores, leaving a gap in the evaluation of smaller fractured rock fragments. This paper investigates the application of advanced segmentation techniques, including an enhanced version of YOLOv8, for core and fracture segmentation. The proposed model effectively addresses challenges associated with fine fractured particles and multi-scale feature extraction, improving segmentation accuracy and reliability. Results demonstrate that the enhanced YOLOv8 model outperforms traditional methods in extracting core fragments, offering a more scalable and efficient solution for fracture assessment in drilling operations. |
| Author | Yuan, Zhong Duan, Longchen Gao, Hui Tan, Songheng |
| Author_xml | – sequence: 1 givenname: Zhong surname: Yuan fullname: Yuan, Zhong email: yuanzhong@cug.edu.cn organization: School of Future Technology, China University of Geosciences,Wuhan,China,430074 – sequence: 2 givenname: Longchen surname: Duan fullname: Duan, Longchen email: duanlongchen@cug.edu.cn organization: China University of Geosciences,Faculty of Engineering,Wuhan,China,430074 – sequence: 3 givenname: Hui surname: Gao fullname: Gao, Hui email: gaohui@cug.edu.cn organization: China University of Geosciences,Faculty of Engineering,Wuhan,China,430074 – sequence: 4 givenname: Songheng surname: Tan fullname: Tan, Songheng email: wstansongcheng@cug.edu.cn organization: China University of Geosciences,Faculty of Engineering,Wuhan,China,430074 |
| BookMark | eNo1z81KxDAUQOEoCk5H30CwL9B6b_PTZClxRgcKXTgbV0Pa3tRIm0pbBN9eQV2d3QcnYRdxisTYHUJecIPm3lqrhAaTF1DIHBFLzaE8Y4nRupQaFcpztkHDRYal0lcsWZZ3AAUG-YaJXXxzsaUufa2r-lOnfprTxzkMQ4h9aqeZ0sPoekpfqB8prm4NU7xml94NC938dcuO-93RPmdV_XSwD1UWDF-zjn5YoTrvjGzBC-4QjO-MgMJzhUKAKggIvGpQdUVJTdt6RY1sWqklOL5lt79sIKLTxxxGN3-d_g_5N9Z-Ryg |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.23919/CCC64809.2025.11178307 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Xplore POP ALL IEEE Xplore All Conference Proceedings IEEE/IET Electronic Library (IEL) (UW System Shared) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISBN | 9887581615 9789887581611 |
| EISSN | 1934-1768 |
| EndPage | 7779 |
| ExternalDocumentID | 11178307 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Key Research and Development Program of China grantid: 2023YFC3007003 funderid: 10.13039/501100012166 – fundername: National Natural Science Foundation of China grantid: 42272366 funderid: 10.13039/501100001809 |
| GroupedDBID | 29B 6IE 6IF 6IK 6IL 6IN AAJGR AAWTH ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IPLJI OCL RIE RIL |
| ID | FETCH-LOGICAL-i93t-deced46dfa95c0f43a109fd9402f36144062e0e0f6b16d27ebccf6eb5bc5850a3 |
| IEDL.DBID | RIE |
| IngestDate | Wed Oct 15 14:21:28 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i93t-deced46dfa95c0f43a109fd9402f36144062e0e0f6b16d27ebccf6eb5bc5850a3 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_11178307 |
| PublicationCentury | 2000 |
| PublicationDate | 2025-July-28 |
| PublicationDateYYYYMMDD | 2025-07-28 |
| PublicationDate_xml | – month: 07 year: 2025 text: 2025-July-28 day: 28 |
| PublicationDecade | 2020 |
| PublicationTitle | Chinese Control Conference |
| PublicationTitleAbbrev | CCC |
| PublicationYear | 2025 |
| Publisher | Technical Committee on Control Theory, Chinese Association of Automation |
| Publisher_xml | – name: Technical Committee on Control Theory, Chinese Association of Automation |
| SSID | ssj0060913 |
| Score | 2.298886 |
| Snippet | The exploration of deep formations by drilling has brought the issue of rock fragment into focus, emphasizing the need for accurate assessment of the fragment... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 7774 |
| SubjectTerms | Accuracy Cascade Encoder-Decoder Convolutional neural networks Core Analysis Deep learning Drilling Feature extraction Image segmentation Reliability Rocks Segmentation Surface treatment Switches YOLOv8 |
| Title | Enhanced YOLOv8 for Drilling Core Image Segmentation |
| URI | https://ieeexplore.ieee.org/document/11178307 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwMhECbaeNCLrxrf4eB1W94s57WNJk3bxMbUUwMsaA_dmrrt7xdo6-PgwRshAcIQZvhm-GYAuCOKldZiljHudcYCdsuMQy6gFGIJdVbL5Bp47sl-Px-P1XBDVk9cGOdc-nzmWrGZYvnl3C6jq6wd7qXMaeSO70op1mStrdoVMcHl-gMXoQqrdlEUguUoklEIb22H_iqikmxI9_Cfqx-B5jcbDw6_7Mwx2HHVCTj4kUjwFLBO9ZZC-fBl0BuschieovB-MU0Jt2ERVoKPs6A54JN7nW3YRlUTjLqdUfGQbeohZFNF66x0YRomSq8Vt8gzqjFSvlQBAXoacR0SJIgZeWGwKIl0xlovnOHGBkyAND0DjWpeuXMAwyQeS8oV4SYAPK4DDjbImiAvrXOML0Az7n_yvs54Mdlu_fKP_iuwH6UcfZ4kvwaNerF0N2DPrurpx-I2ndMnP6iS4g |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LTwIxEG4MmqgXXxjf9uB1odvXbs8rBOIKJBKDJ9J2W90Di0Hg99sW8HHw4K1p0jadpjP9ZvrNAHCHBS20jmlEmZURddgtUgYZh1KwxsRomQTXwHOe9HrpaCQGa7J64MIYY8LnM9PwzRDLL6Z64V1lTXcvk5R47vg2oxSjFV1ro3i5T3G5-sKFiYhFM8syTlPk6SiYNTaDf5VRCVakffDP9Q9B_ZuPBwdfluYIbJnqGOz_SCV4AmiregvBfPjSz_vLFLrHKLyflSHlNszcSrA7cboDPpnXyZpvVNXBsN0aZp1oXREhKgWZR4Vx01BeWCmYRpYSGSNhC-EwoCUe2SGOnaCR5SrmBU6M0tpyo5jSDhUgSU5BrZpW5gxAN4mNE8IEZspBPCYdElZIKycvKdM4Pgd1v__x-yrnxXiz9Ys_-m_Bbmf4mI_zbu_hEux5iXsPKE6vQG0-W5hrsKOX8_JjdhPO7BMXUpYp |
| 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=proceeding&rft.title=Chinese+Control+Conference&rft.atitle=Enhanced+YOLOv8+for+Drilling+Core+Image+Segmentation&rft.au=Yuan%2C+Zhong&rft.au=Duan%2C+Longchen&rft.au=Gao%2C+Hui&rft.au=Tan%2C+Songheng&rft.date=2025-07-28&rft.pub=Technical+Committee+on+Control+Theory%2C+Chinese+Association+of+Automation&rft.eissn=1934-1768&rft.spage=7774&rft.epage=7779&rft_id=info:doi/10.23919%2FCCC64809.2025.11178307&rft.externalDocID=11178307 |