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林业科学 ›› 2018, Vol. 54 ›› Issue (11): 134-142.doi: 10.11707/j.1001-7488.20181119

• 论文与研究报告 • 上一篇    下一篇

人造板表面缺陷检测图像自适应快速阈值分割算法

郭慧1, 王霄1, 刘传泽2, 周玉成1,2   

  1. 1. 中国林业科学研究院木材工业研究所 北京 100091;
    2. 山东建筑大学信息与电气工程学院 济南 250101
  • 收稿日期:2018-04-02 修回日期:2018-06-05 出版日期:2018-11-25 发布日期:2018-12-04
  • 基金资助:
    中国林业科学研究院中央级公益性科研院所基本科研业务费专项资金(CAFYBB2018MB002);泰山学者优势特色学科人才团队(2015162)。

Research on Adaptive Fast Threshold Segmentation Algorithm for Surface Defect Detection of Wood-Based Panel

Guo Hui1, Wang Xiao1, Liu Chuanze2, Zhou Yucheng1,2   

  1. 1. Research Institute of Wood Industry, CAF Beijing 100091;
    2. School of Information and Electrical Engineering, Shandong Jianzhu University Jinan 250101
  • Received:2018-04-02 Revised:2018-06-05 Online:2018-11-25 Published:2018-12-04

摘要: [目的]提出一种自适应快速阈值图像分割算法,为人造板表面缺陷在线检测提供支持。[方法]首先将整幅图像划分成若干子区域,通过计算子区域的方差对缺陷进行定位,提取出缺陷所在区域,只对缺陷区域进行图像分割,解决小面积目标难以准确分割的问题。然后对缺陷区域的一维灰度直方图进行处理,直方图平滑后去除掉不显著波峰,根据处理后保留的主要波峰数量和位置自适应地确定分割阈值个数以及每个阈值的分割区间,实现当图像中出现多种类型缺陷时算法自动确定分割阈值个数。最后,通过分析Otsu算法,将阈值穷举搜索改进为条件搜索并限定搜索方向,在每个分割区间内使用改进的Otsu算法对阈值进行搜索,提高搜索速度。[结果]对板面存在油污、大刨花、胶斑、杂物、松软5种类型缺陷的人造板表面图像进行分割,在板面缺陷数量、类型不固定的情况下,算法可以自适应地确定分割阈值个数,在15 ms内将各种类型缺陷从人造板表面图像中分割出来,平均分割准确率达97%。[结论]自适应快速阈值分割算法能够快速、准确将缺陷从人造板表面图像中分离出来,在执行速度和分割效果上均满足在线缺陷检测系统的要求,可为人造板表面缺陷在线检测提供新思路。

关键词: Otsu阈值分割算法, 图像分割, 人造板表面缺陷检测

Abstract: [Objective] An adaptive fast thresholding image segmentation algorithm was proposed in this paper, which could quickly and accurately separate the defects from the surface images of wood-based panels,and provide support for on-line detection of wood-based panel surface defects.[Method] Firstly,the algorithm divided the whole image into several sub-regions. Secondly,the defect areas were located by calculating the variance of each sub-region. And then,the image segment was only done in defect areas for solving the problem of accurate segmentation of small targets.The one-dimension gray scale histogram of extracted defect area was processedusing histogram smoothing to remove the non-significant peaks.According to the main wave peaks reserved in the histogram after the processing,the number of the thresholds and segmentation interval for each threshold were determined adaptively. At each interval,the threshold was searched using an improved fast Otsu segmentation algorithm.Through the analysis of the Otsu algorithm,the threshold was found by a conditional search instead of the exhaustive search and the search direction was specified. In each segmentation interval, the improved fast Otsu segmentation algorithm was used to search the threshold,which improved the search speed.[Result] The segmentations of surface images of wood-based panels with five types of defects such as oil stains,big wood shavings,glue spots,sundries and loose regions were done using the adaptive fast algorithm proposed in this paper. Although the number and type of the defects were not fixed,this algorithm still could determine the number of the thresholds automatically. All kinds of defects were separated from the surface images in 15 ms with a above 97% segmentation accuracy rate.[Conclusion] The adaptive fast threshold segmentation algorithm presented in this paper can quickly and accurately separate the defects from the surfaces of the panels,and the execution speed and the segmentation effect meets the requirements of the on-line defect detection system. It provides a new approach for automatic on-line detection of surface defects on wood-based panels.

Key words: Otsu threshold segmentation algorithm, image segmentation, surface defect detection of wood-based panel

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