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...

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Published in:Chinese Control Conference pp. 7774 - 7779
Main Authors: Yuan, Zhong, Duan, Longchen, Gao, Hui, Tan, Songheng
Format: Conference Proceeding
Language:English
Published: Technical Committee on Control Theory, Chinese Association of Automation 28.07.2025
Subjects:
Accuracy
Cascade Encoder-Decoder
Convolutional neural networks
Core Analysis
Deep learning
Drilling
Feature extraction
Image segmentation
Reliability
Rocks
Segmentation
Surface treatment
Switches
YOLOv8
ISSN:1934-1768
Online Access:Get full text
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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
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  givenname: Songheng
  surname: Tan
  fullname: Tan, Songheng
  email: wstansongcheng@cug.edu.cn
  organization: China University of Geosciences,Faculty of Engineering,Wuhan,China,430074
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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...
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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
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