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<p>Internal wood defects pose a&#xa0;serious threat to forest health, reduce timber quality, and increase resource waste during processing, thereby constraining the sustainable development of the wood industry. Computed tomography (CT) enables non-destructive, high-resolution visualization of internal structures, yet accurate defect identification and quantitative characterization remain challenging. This study proposed an integrated framework for internal defect detection and 3D reconstruction in industrial wood applications. The proposed SCV-YOLOv8n model, built upon the lightweight YOLOv8n‑seg architecture, integrated a Spatial Pyramid Pooling Faster Cross Stage Partial Channel (SPPFCSPC) module and a Convolutional Block Attention Module (CBAM) to enhance feature extraction and improve segmentation robustness for complex defect textures. Furthermore, an improved Visualization Toolkit (VTK) pipeline was developed for high-precision 3D reconstruction. The upgraded VTK pipeline integrated Backbone-Enhanced Segmentation Refinement (BESR), Feature Connectivity Adjustment (FCA), and Boundary Curvature Guidance (BCG) submodules, which collectively refine boundary continuity, strengthen internal coherence, and preserve curvature consistency, resulting in more faithful representations of internal geometries. Experiments conducted on three typical internal defect types (decay, hollow, and knots) demonstrated that SCV‑YOLOv8n outperformed the baseline YOLOv8n-seg in both detection accuracy and segmentation consistency, while the improved VTK pipeline achieved higher 3D reconstruction fidelity and enhanced morphological realism. The 3D reconstruction results revealed that decay and hollow defects form interpenetrating cylindrical structures, whereas knots present irregular localized volumes. This framework provides an effective and practical method for accurate defect visualization, sawing path optimization, and resource utilization, supporting sustainable and intelligent wood processing.</p>

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Deep learning-driven 3D reconstruction of internal wood defects from CT images

  • 期刊论文

  • 发表日期：

    2026年3月5日

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  • Guangqiang Xie,
  • Yuanda Qi,
  • Ping Zhang,
  • Chunmei Yang,
  • Yaoxiang Li,
  • Lihai Wang

摘要

Internal wood defects pose a serious threat to forest health, reduce timber quality, and increase resource waste during processing, thereby constraining the sustainable development of the wood industry. Computed tomography (CT) enables non-destructive, high-resolution visualization of internal structures, yet accurate defect identification and quantitative characterization remain challenging. This study proposed an integrated framework for internal defect detection and 3D reconstruction in industrial wood applications. The proposed SCV-YOLOv8n model, built upon the lightweight YOLOv8n‑seg architecture, integrated a Spatial Pyramid Pooling Faster Cross Stage Partial Channel (SPPFCSPC) module and a Convolutional Block Attention Module (CBAM) to enhance feature extraction and improve segmentation robustness for complex defect textures. Furthermore, an improved Visualization Toolkit (VTK) pipeline was developed for high-precision 3D reconstruction. The upgraded VTK pipeline integrated Backbone-Enhanced Segmentation Refinement (BESR), Feature Connectivity Adjustment (FCA), and Boundary Curvature Guidance (BCG) submodules, which collectively refine boundary continuity, strengthen internal coherence, and preserve curvature consistency, resulting in more faithful representations of internal geometries. Experiments conducted on three typical internal defect types (decay, hollow, and knots) demonstrated that SCV‑YOLOv8n outperformed the baseline YOLOv8n-seg in both detection accuracy and segmentation consistency, while the improved VTK pipeline achieved higher 3D reconstruction fidelity and enhanced morphological realism. The 3D reconstruction results revealed that decay and hollow defects form interpenetrating cylindrical structures, whereas knots present irregular localized volumes. This framework provides an effective and practical method for accurate defect visualization, sawing path optimization, and resource utilization, supporting sustainable and intelligent wood processing.