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Related Concept Videos

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Softwoods and Hardwoods

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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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Structural Properties and Dimensions of Lumber01:21

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Wood's structural properties derive from fibers aligned along the tree's length, contributing significantly to its mechanical strength. Wood exhibits up to twenty times greater tensile strength along these fibers compared to across them, and generally shows better performance under compression than tension. The length of fibers varies, with hardwoods having fibers around one twenty-fifth inch long and softwoods ranging from one-eighth to one-third inch.
The strength characteristics of...
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Introduction to Wood01:19

Introduction to Wood

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Wood, derived from trees, is a versatile and widely used construction material. Trees feature a trunk surrounded by a protective layer of dead bark. Beneath this outer layer lies the living bark, followed by the cambium, and then the sapwood which transitions into heartwood as it matures. At the center of the trunk is the pith. The age of a tree can be discerned by examining its growth rings, which are concentric bands visible in the trunk's cross-section.
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Wood Surfacing01:14

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Wood surfacing is a critical finishing process designed to smoothen the wood surface, enhance its dimensional accuracy, and make handling safer. This process compensates for potential shrinkage during the seasoning phase by marginally increasing the wood dimensions before surfacing. It also helps correct some distortions that may occur as the wood dries.
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Wood Products01:21

Wood Products

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Wood products encompass a broad range of materials crafted from wood strands, veneers, lumber, and even waste wood-like shreds, designed for both structural and nonstructural purposes. Various specialized wood products have been developed to enhance strength, durability, and versatility in building applications.
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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Label-free in situ Imaging of Lignification in Plant Cell Walls
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Online Color Classification System of Solid Wood Flooring Based on Characteristic Features.

Zilong Zhuang1, Ying Liu1, Fenglong Ding1

  • 1College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|January 9, 2021
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Summary

Machine vision and deep learning automate solid wood flooring color sorting, boosting efficiency. XGBoost achieved 97.22% accuracy in classifying wood floor colors, reducing manual labor costs.

Keywords:
XGBoostcolor featurewood color classification

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Materials Science

Background:

  • Solid wood flooring requires color sorting for aesthetic interior design.
  • Manual sorting is costly and inefficient, hindering customization.
  • Automating this process is crucial for meeting diverse customer needs.

Discussion:

  • Machine vision and deep learning methods, including VGG16, DenseNet121, and XGBoost, were employed for automated color classification.
  • Data augmentation techniques were used to expand the dataset of 108 wood floor images.
  • The study compared the performance of different deep learning models for color grading.

Key Insights:

  • XGBoost demonstrated high accuracy (97.22%) in classifying solid wood flooring colors.
  • The XGBoost model achieved rapid processing with an average test time of 51 ms per image.
  • The developed system significantly reduces manual sorting costs and enhances production efficiency.

Outlook:

  • This automated approach can be applied to other wood-based materials requiring color grading.
  • Further research could explore real-time sorting integration on production lines.
  • Optimizing deep learning models may lead to even higher classification accuracy and efficiency.