基于Faster R-CNN模型的木材表面节子缺陷检测

萧嘉奕; 马艳; 周竹; 方益明

doi:10.19455/j.mcgy.20200211

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基于Faster R-CNN模型的木材表面节子缺陷检测
Detection of Timber Surface Knots Based on Faster R-CNN
1. 浙江农林大学信息工程学院
2. 浙江省林业智能监测与信息技术研究重点实验室
3. 浙江双枪竹木有限公司
2020年34卷第2期页码：45-48
DOI： 10.19455/j.mcgy.20200211

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[1]萧嘉奕,马艳,周竹,方益明.基于Faster R-CNN模型的木材表面节子缺陷检测[J].木材工业,2020,34(02):45-48.

XIAO Jia-yi, MA Yan, ZHOU Zhu, et al. Detection of Timber Surface Knots Based on Faster R-CNN[J]. China Wood Industry, 2020,34(2):45-48.
[1]萧嘉奕,马艳,周竹,方益明.基于Faster R-CNN模型的木材表面节子缺陷检测[J].木材工业,2020,34(02):45-48. DOI： 10.19455/j.mcgy.20200211.

XIAO Jia-yi, MA Yan, ZHOU Zhu, et al. Detection of Timber Surface Knots Based on Faster R-CNN[J]. China Wood Industry, 2020,34(2):45-48. DOI： 10.19455/j.mcgy.20200211.

摘要

研究基于Faster R-CNN模型的木材表面节子缺陷检测方法,解决现有方法存在的特征选取困难、不能适应木材以及节子的多样性变化、检测精度不高等问题,而且能够给出节子缺陷的位置以及图像的大小。小样本集测试结果表明,该方法能够取得较高的检测精度,准确率为94.0%;对其中4个典型样本进行分析,位置检测最大误差仅为5个像素,大小检测最大误差仅为7个像素;相比OSTU方法具有更好的检测精度。

Abstract

An inspection method of timber surface knots was developed based on Faster R-CNN model. The new method made the selection of knot features data-driven, adapted variation of wood species and knot types, and improved inspection accuracy efficiently. The test results showed that the positions and sizes of the knots were detected with 94.0% of inspection accuracy. The images of the four typical samples showed that the maximum errors of knot positions and sizes were 5 pixels and 7 pixels, respectively. The Faster R-CNN method was superior to OSTU-based method in the study.