Image detection of wood surface defects based on deep learning

Xian-ming CHEN; A-chuan WANG; Chun-yan WANG

doi:10.3788/YJYXS20193409.0879

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Image detection of wood surface defects based on deep learning

Image Processing | 更新时间：2020-07-05

- Image detection of wood surface defects based on deep learning
- Chinese Journal of Liquid Crystals and Displays Vol. 34, Issue 9, Pages: 879-887(2019)
- 作者机构：
  
  1.东北林业大学信息与计算机工程学院, 黑龙江哈尔滨 150040
  2.扎兰屯职业学院, 内蒙古扎兰屯 162650
- 作者简介：
- 基金信息：
- DOI：10.3788/YJYXS20193409.0879
  CLC： TP391.41
- Received：12 April 2019，
  
  Accepted：19 June 2019，
  
  Published：05 September 2019
- 稿件说明：
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Xian-ming CHEN, A-chuan WANG, Chun-yan WANG. Image detection of wood surface defects based on deep learning[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 879-887.
DOI：

Xian-ming CHEN, A-chuan WANG, Chun-yan WANG. Image detection of wood surface defects based on deep learning[J]. Chinese journal of liquid crystals and displays, 2019, 34(9): 879-887. DOI： 10.3788/YJYXS20193409.0879.

摘要

针对木材活节、死节、虫眼等缺陷图像的检测问题，本文提出了一种基于深度学习的木材缺陷图像检测方法。首先，通过对Faster-RCNN网络进行训练，得到了可以对木材缺陷定位和识别的检测模型；然后，应用NL-Means方法对图像进行去噪，通过线性滤波、调整对比度和亮度实现图像增强；再对图像进行二值化处理，根据像素值差异提取缺陷边缘特征点集，实现了对木材缺陷的精细分割；最后，对椭圆拟合方法进行了改进，实现了对木材缺陷边缘点集的椭圆拟合，提供了新的木材缺陷加工方案。实验结果表明，该算法具有较好的木材缺陷定位和分类能力，得到了较好的分割及拟合效果，可在缺陷修补这一环节减少约10%的木材填充量。

Abstract

Aiming at the defect image detection of live knots

dead knots

wormhole and so on

a wood defect image detection method based on depth learning was proposed. Firstly

by training Faster-RCNN network

a detection model for locating and recognizing wood defects is obtained. Secondly

the image is denoised by NL-Means method

and image enhancement is achieved by linear filtering

adjusting contrast and brightness. Thirdly

the image is processed by binarization

and the edge feature points of defects are extracted according to the difference of pixel values to realize wood defects fine segmentation. Finally

the ellipse fitting method is improved to realize the ellipse fitting of wood defect edge point set

and a new wood defect processing scheme is provided. The experimental results show that the algorithm has better wood defect location and classification ability

and gets better segmentation and fitting effect. The filling volume of wood can be reduced by about 10% in the process of defect repair.

关键词

Keywords

references

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