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Related Concept Videos

Lumber Defects01:23

Lumber Defects

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Lumber defects, which can affect both the appearance and structural integrity of wood, include a variety of growth and manufacturing flaws. Growth defects such as knots and knotholes occur where branches were once attached to the tree trunk, with knotholes forming when these knots fall out. Other natural defects include decay and insect damage, which compromise the wood's strength and durability.
Shakes are minor fractures that run along or across the wood's annual rings, while wane is...
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Softwoods and hardwoods, derived from different types of trees, are distinguished by their leaf structures and cellular compositions, each serving unique purposes in construction and manufacturing. Softwoods come from cone-bearing trees with needle-like leaves and are predominantly composed of longitudinal cells called tracheids and a smaller proportion of radial cells known as rays. Due to their cellular structure, softwoods are commonly used in construction for structural frames, sheathing,...
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Defect-Mask2Former: An Improved Semantic Segmentation Model for Precise Small-Sized Defect Detection on Large-Sized

Mingming Qin1,2, Hongxu Li2, Yuxiang Huang1,2

  • 1College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming 650224, China.

Sensors (Basel, Switzerland)
|April 14, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an improved Defect-Mask2Former model for precise wood surface defect segmentation in glued laminated timber (GLT) quality grading. The enhanced model significantly reduces miss rates and size measurement errors, improving grading accuracy and efficiency.

Keywords:
attention-guided pyramid enhancementdefect boundary calibrationglued laminated timber gradingsmall-sized defectswood defect segmentation

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Area of Science:

  • Computer Vision
  • Materials Science
  • Artificial Intelligence

Background:

  • Accurate segmentation of small wood surface defects is crucial for glued laminated timber (GLT) quality grading.
  • Existing semantic segmentation models struggle with high miss rates, blurred boundaries, and size measurement inaccuracies for small defects.

Purpose of the Study:

  • To develop an improved Defect-Mask2Former model for precise pixel-level segmentation of small wood surface defects.
  • To address the limitations of current models in miss rates, boundary localization, and size measurement errors.
  • To enhance the efficiency and accuracy of GLT quality grading through automated defect detection.

Main Methods:

  • Proposed an improved Defect-Mask2Former model incorporating an Attention-Guided Pyramid Enhancement (AGPE) module and a Defect Boundary Calibration and Correction (DBCC) module.
  • Developed a custom image acquisition device and constructed the PlankDefSeg dataset with 3500 annotated images.
  • Evaluated the model's performance on the PlankDefSeg dataset, measuring mean Intersection over Union (mIoU), miss rate, size measurement error, and inference speed.

Main Results:

  • The Defect-Mask2Former model achieved an mIoU of 85.34% for small defects, a 17.84% improvement over the baseline.
  • Miss rate decreased from 20.78% to 5.83%, and size measurement error was 2.86%, meeting the GB/T26899-2022 standard.
  • The model achieved 27.6 FPS inference speed, and integration into GLT grading improved accuracy to 94.3% and reduced processing time by 20-fold.

Conclusions:

  • The proposed Defect-Mask2Former model offers precise pixel-level segmentation for small wood surface defects, significantly improving GLT quality grading.
  • The study provides a robust solution for intelligent GLT grading, demonstrating substantial improvements in accuracy and efficiency.
  • The developed model and dataset serve as a valuable reference for industrial surface defect segmentation tasks.