1.College of Engineering and Technology， Northeast Forestry University， Harbin 150040， Heilongjiang， China
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肖雨晴,杨慧敏,王柯欣等.卷积神经网络在木材缺陷检测应用中的研究进展[J].木材科学与技术,2021,35(03):12-18.
XIAO Yu-qing,YANG Hui-min,WANG Ke-xin,et al.Research Progress of Convolutional Neural Network in Wood Defect Detection[J].Chinese Journal of Wood Science and Technology,2021,35(03):12-18.
木材缺陷的快速检测和精准定位是实现木材加工机械化、一体化的首要条件。采用卷积神经网络（CNN）检测木材缺陷，不仅可以克服人工检测效率低、准确率低的问题，还可以节省劳动力、提高木材检测的智能化水平。本文概述了CNN的理论和典型网络模型，梳理、总结了CNN在木材缺陷图像分割、特征提取、识别分类中的研究与应用现状，并对CNN在木材缺陷检测领域的发展趋势进行展望，进一步拓展卷积神经网络在木材缺陷检测中的应用。
Efficiency and accuracy of detecting locations of wood defects are the prerequisite for mechanization and integration of wood processing. Using the convolutional neural network(CNN) to detect wood defects can not only increase the efficiency and accuracy comparing to manual detecting processes, but also save labor costs, as well as improve the intelligent level of the wood defect detection. In this paper, the theories and typical models of CNN were introduced; the application and research status of CNN in wood defect image segmentation, feature extraction, recognition and classification were summarized; and the development trends of CNN in the field of wood defect detection were proposed.
卷积神经网络木材缺陷图像处理深度学习
convolutional neural network(CNN)wood defectsimage processingdeep learning
林文树. 应力波与超声波在木材内部缺陷检测中的对比研究[D]. 哈尔滨: 东北林业大学, 2005.
张甜, 程小武, 陆伟东, 等. 超声波法检测木材内部孔洞缺陷的研究[J]. 西南林业大学学报, 2016, 36(1): 121-125,130.
ZHANG T, CHENG X W, LU W D, et al. Experimental study on testing internal hole defects of wood by ultrasonic method[J]. Journal of Southwest Forestry University, 2016, 36(1): 121-125,130.
戚玉涵, 葛浙东, 张星梅, 等. 工业CT扫描在原木内部缺陷检测中应用[J]. 木材工业, 2018, 32(6): 38-40.
QI Y H, GE Z D, ZHANG X M, et al. Log internal defect detection using industrial CT scanning[J]. China Wood Industry, 2018, 32(6): 38-40.
邵明伟，董军宇. 基于深度学习的木材优选锯视觉检测算法[J]. 林业科学，2020，56(12)：123-129.
SHAO M W, DONG J Y. A new algorithm for automatic optimiaing cross-cut saw based on deep learning algorithm[J]. Scientia Silvae Sinicae, 2020, 56(12): 123-129.
肖雨晴，杨慧敏. 目标检测算法在交通场景中应用综述[J]. 计算机工程与应用，2021，57(6)：30-41.
XIAO Y Q, YANG H M. Research on application of object detection algorithm in traffic scene[J]. CEA, 2021, 57(6): 30-41.
Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
Park S B, Lee J W, Kim S K. Content-based image classification using a neural network[J]. Pattern Recognition Letters, 2004, 25(3): 287-300.
Vailaya A, Zhang H J, Yang C J, et al. Automatic image orientation detection[J]. IEEE Transactions on Image Processing, 2002, 11(7): 746-755.
Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: A brief review[J]. Computational Intelligence and Neuroscience, DOI：10.1155/2018/7068349http://dx.doi.org/10.1155/2018/7068349.
赖菲，罗廷芳，丁锐，等. 图像处理技术在木材表面缺陷检测中的应用[J]. 林业机械与木工设备，2021，49(2)：16-21.
LAI F, LUO T F, DING R, et al. Application of image processing technology to wood surface defect detection[J]. Forestry Machinery &Woodworking Equipment, 2021, 49(2): 16-21.
Lecun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11)：2278-2324.
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]. Lake Tahoe: Neural Information Processing Systems, 2012.
Simonyan K, Zisserman A. Very deep convolutioal networks for large scale image recognition[C]. International Conference on Learning Rep-resentations, 2015.
HE K M, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]. Las Vegas: IEEE Conference on Computer Vision & Pattern Recognition. IEEE Computer Society, 2016.
张力超, 马蓉, 张垚鑫. 改进的LeNet-5模型在苹果图像识别中的应用[J]. 计算机工程与设计, 2018, 39(11): 3570-3575.
ZHANG L C, MA R, ZHANG Y X. Application of improved LeNet-5 model in apple image recognition[J]. Computer Engineering and Design, 2018, 39(11): 3570-3575.
张雪芹, 陈嘉豪, 诸葛晶晶, 等. 基于深度学习的快速植物图像识别[J]. 华东理工大学学报(自然科学版), 2018, 44(6): 887-895.
ZHANG X Q, CHEN J H, ZHUGE J J, et al. Deep learning based fast plant image recognition[J]. Journal of East China University of Science and Technology (Natural Science Edition), 2018, 44(6): 887-895.
罗浩伦, 冯泽霖, 冉钟南, 等. 基于VGG16网络的茶叶嫩芽自动检测研究[J]. 农业与技术, 2020, 40(1): 15-17.
刘翠玲, 林珑, 于重重, 等. 基于深度学习的花生高光谱图像分类方法研究[J]. 计算机仿真, 2020, 37(3): 189-192,283.
LIU C L, LIN L, YU C C, et al. Research on peanut hyperspectral image classification method based on deep learning[J]. Computer Simulation, 2020, 37(3): 189-192,283.
范佳楠, 刘英, 胡忠康, 等. 基于Faster R-CNN的实木板材缺陷检测识别系统[J]. 林业工程学报, 2019, 4(3): 112-117.
FAN J N, LIU Y, HU Z K, et al. Solid wood panel defect detection and recognition system based on Faster R-CNN[J]. Journal of Forestry Engineering, 2019, 4(3): 112-117.
WEI L, LIPING S. An improved binarization algorithm of wood image defect segmentation based on non-uniform background[J]. Journal of Forestry Research, 2019, 30(4): 1527-1533.
张益翔. 基于小波变换和LBP的木材表面缺陷识别[D]. 南京：南京林业大学，2017.
王金聪, 宋文龙, 张彭涛. 应用改进分水岭算法对木材表面缺陷图像分割试验[J]. 东北林业大学学报, 2018, 46(10): 93-97.
WANG J C, SONG W L, ZHANG P T. Image segmentation of wood surface defects with improved watershed algorithm[J]. Journal of Northeast Forestry University, 2018, 46(10): 93-97.
刘忠伟, 戚大伟. 基于卷积神经网络的树种识别研究[J]. 森林工程, 2020, 36(1): 33-38.
LIU Z W, QI D W. Study on tree species identification based on convolution neural network[J]. Forest Engineering, 2020, 36(1): 33-38.
程玉柱, 顾权, 王众辉, 等. 基于深度学习的木材缺陷图像检测方法[J]. 林业机械与木工设备, 2018, 46(8): 33-36.
CHENG Y Z, GU Q, WANG Z H, et al. Wood defect image segmentation based on deep learning[J]. Forestry Machinery & Woodworking Equipment, 2018, 46(8): 33-36.
张瑞峰. 基于机器视觉的木材缺陷无损检测研究[D]. 杭州：浙江农林大学，2019.
王红军, 黎邹邹, 邹湘军. 基于Adaboost与CNN的木材表面缺陷检测[J]. 系统仿真学报, 2019, 31(8): 1636-1645.
WANG H J, LI Z Z, ZOU X J. Wood surface defect detection based on adaboost and CNN[J]. Journal of System Simulation, 2019, 31(8): 1636-1645.
刘英, 周晓林, 胡忠康, 等. 基于优化卷积神经网络的木材缺陷检测[J]. 林业工程学报, 2019, 4(1): 115-120.
LIU Y, ZHOU X L, HU Z K, et al. Wood defect recognition based on optimized convolution neural network algorithm[J]. China Forestry Science and Technology, 2019, 4(1): 115-120.
WANG X J, WU S H, LIU Y P. Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation[C]. Qingdao: Ninth international conference on graphic and image processing, 2017.
罗微, 孙丽萍. 利用局部二值模式和方向梯度直方图融合特征对木材缺陷的支持向量机学习分类[J]. 东北林业大学学报, 2019, 47(6): 70-73.
LUO W, SUN L P. Wood defect detection and classification by fusion feature and support vector machine[J]. Journal of Northeast Forestry University, 2019, 47(6): 70-73.
许超. 基于特征融合与压缩感知的实木地板缺陷检测方法研究[D]. 哈尔滨: 东北林业大学, 2015.
HE T, LIU Y, YU Y B, et al. Application of deep convolutional neural network on feature extraction and detection of wood defects[J]. Measurement, DOI：10.1016/j.measurement.2019.107357http://dx.doi.org/10.1016/j.measurement.2019.107357.
刘嘉政，王雪峰，王甜. 基于深度学习的5种树皮纹理图像识别研究[J]. 北京林业大学学报，2019，41(4)：146-154.
LIU J Z, WANG X F, WANG T. Research on image recognition of five bark texture images based on deep learning[J]. Journal of Beijing Forestry University, 2019, 41(4): 146-154.
Barmpoutis P, Dimitropoulos K, Barboutis I, et al. Wood species recognition through multidimensional texture analysis[J]. Computers and Electronics in Agriculture, 2018(144):241-248.
Hashim U R, Hashim S Z M, Muda A K, et al. Extraction and exploratory analysis of texture features on images of timber defect[J]. Advanced Science Letters, 2018, 24(2): 1104-1108.
晁晓菲, 樊李行, 蔡骋, 等. 基于多特征提取和选择的木材分类与识别[J]. 现代农业科技, 2018(18): 118-120.
CHAO X F, FAN L H, CAI C, et al. Wood texture classification and identification based on multi-feature extraction and selection[J]. Modern Agricultural Science and Technology, 2018(18): 118-120.
陈献明, 王阿川, 王春艳. 基于深度学习的木材表面缺陷图像检测[J]. 液晶与显示, 2019, 34(9): 879-887.
CHEN X M, WANG A, WANG C Y. Image detection of wood surface defects based on deep learning[J]. Chinese Journal of Liquid Crystals and Displays, 2019, 34(9): 879-887.
白雪冰, 王克奇, 王业琴, 等. 基于BP神经网络的木材表面颜色特征分类的研究[J]. 森林工程, 2007, 23(1): 24-26.
BAI X B, WANG K Q, WANG Y Q, et al. Research of the feature classification of wood surface colors based on neural network[J]. Forest Engineering, 2007, 23(1): 24-26.
Tafarroj M M, Kalani H, Moavenian M, et al. An application of principal component analysis method in wood defects identification[J]. Journal of the Indian Academy of Wood Science, 2014, 11(1): 33-38.
HU G S, YIN C J, WAN M Z, et al. Recognition of diseased Pinus trees in UAV images using deep learning and AdaBoost classifier[J]. Biosystems Engineering, 2020(194):138-151.
JUNG S Y, TSAI Y H, CHIU W Y, et al. Defect detection on randomly textured surfaces by convolutional neural networks[C]. Auckland: IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2018.
萧嘉奕，马艳，周竹，等. 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(2)：45-48.
XIAO J Y, MA Y, ZHOU Z, et al. Detection of timber surface knots based on Faster R-CNN[J]. China Wood Industry, 2020, 34(2): 45-48.
彭煜, 肖书浩, 阮金华, 等. 基于Faster R-CNN的刨花板表面缺陷检测研究[J]. 组合机床与自动化加工技术, 2020(3): 91-94.
PENG Y, XIAO S H, RUAN J H, et al. Research on surface defect detection of particleboard based on faster R-CNN[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2020(3): 91-94.
Urbonas A, Raudonis V, Maskeliunas R, et al. Automated identification of wood veneer surface defects using faster region-based convolutional neural network with data augmentation and transfer learning[J]. Applied Sciences, 2019, 9(22): 4849.
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