1.南京林业大学家居与工业设计学院，江苏南京 210037
2.江苏省林业资源高效加工利用协同创新中心， 江苏南京 210037
3.中国林业科学研究院林产化学工业研究所，江苏南京 210042
4.德华兔宝宝装饰新材股份有限公司，浙江德清 313200
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王祺,冯鑫浩,史诗琪等.机器视觉在木制品制造中的应用[J].木材科学与技术,2022,36(05):17-24.
WANG Qi,FENG Xin-hao,SHI Shi-qi,et al.Application of Machine Vision in Manufacturing of Wood Products[J].Chinese Journal of Wood Science and Technology,2022,36(05):17-24.
在概述机器视觉国内外研究现状的基础上，分析基于机器视觉技术的图像采集、特征提取、识别分类等理论与算法研究；重点阐述机器视觉在木制品原材料树种识别、原木检尺和锯材分等、木制品缺陷检测、木制品表面颜色分析等领域的应用，并结合当前家居企业生产制造的发展现状，提出机器视觉在木制品制造领域的发展趋势，为木制品智能制造提供技术基础。
In this study, the theoretical and algorithmic research on the image acquisition, feature extraction, recognition, and classification involved in machine-vision-based wood recognition technology were analyzed based on the development status both in the domestic and international furniture manufacturing. Furthermore, the applications of machine vision in the intelligent manufacturing of wood products, such as the identification of tree species, wood inspection and classification, defect detection, surface color analysis, and quality control of furnishing products were thoroughly examed. The development trend of machine vision in the manufacturing of wood products was put forward with the consideration of the current development of wooden furnishing enterprises. These findings provide a solid foundation for not only the research in the wood science but also the manufacture of wood products.
机器视觉木材工业木制品深度学习缺陷检测质量管控
machine visionwood industrywood productsdeep learningdefects detectionquality control
Smith M L, Smith L N, Hansen M F. The quiet revolution in machine vision - a state-of-the-art survey paper, including historical review, perspectives, and future directions[J]. Computers in Industry, 2021, 130: 103472.
Davis L S. A survey of edge detection techniques[J]. Computer Graphics and Image Processing, 1975, 4(3): 248-270.
Turk M, Pentland A. Eigenfaces for recognition[J]. Journal of Cognitive Neuroscience, 1991, 3(1): 71-86.
Fergus R, Perona P, Zisserman A. Weakly supervised scale-invariant learning of models for visual recognition[J]. International Journal of Computer Vision, 2007, 71(3): 273-303.
Rawat W, Wang Z H. Deep convolutional neural networks for image classification: a comprehensive review[J]. Neural Computation, 2017, 29(9): 2352-2449.
ZHAO Z Q, ZHENG P, XU S T, et al. Object detection with deep learning: a review[J]. IEEE Transactions on Neural Networks and Learning Systems, 2019, 30(11): 3212-3232.
吴义强. 木材科学与技术研究新进展[J]. 中南林业科技大学学报, 2021, 41(1): 1-28.
WU Y Q. Newly advances in wood science and technology[J]. Journal of Central South University of Forestry ＆ Technology, 2021, 41(1): 1-28.
Voulodimos A, Doulamis N, Doulamis A, et al. Deep learning for computer vision: a brief review[J]. Computational Intelligence and Neuroscience, 2018, 2018: 7068349.
陈炜文. 基于机器视觉木材表面缺陷图像分割研究[D]. 广州: 华南农业大学, 2016.
丁安宁, 贺春光, 多化琼, 等. 基于数字图像的木材缺陷识别研究进展[J]. 木材科学与技术, 2022, 36(1): 9-16, 28.
DING A N, HE C G, DUO H Q, et al. Research review of wood defect recognition based on digital images[J]. Chinese Journal of Wood Science and Technology, 2022, 36(1): 9-16, 28.
陈法法, 刘莉莉, 杨蕴鹏, 等. 基于小波和分块同态滤波的变光照图像增强方法[J]. 三峡大学学报(自然科学版), 2022, 44(2): 107-112.
CHEN F F, LIU L L, YANG Y P, et al. Method of variable illumination image enhancement based on wavelet and block homomorphic filtering[J]. Journal of China Three Gorges University (Natural Sciences), 2022, 44(2): 107-112.
Abang M, Ramli M S, Halim S A. Application of blind deconvolution and lucy-Richardson deconvolution for image deblurring[J]. Journal of Fundamental and Applied Sciences, 2018, 9: 232-244.
Teh C H, Chin R T. On the detection of dominant points on digital curves[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(8): 859-872.
吴雨生. 基于机器视觉的结构用锯材分等技术研究[D]. 北京: 中国林业科学研究院, 2020.
章为川, 水鹏朗, 朱磊. 利用各向异性高斯方向导数相关矩阵的角点检测方法[J]. 西安交通大学学报, 2012, 46(11): 91-97.
ZHANG W C, SHUI P L, ZHU L. A corner detection algorithm using anisotropic Gaussian directional derivatives autocorrelation matrix on edge contours[J]. Journal of Xi'an Jiaotong University, 2012, 46(11): 91-97.
王富平, 陈鹏博, 刘卫华, 等. 采用非线性方向导数的混合噪声鲁棒角点检测算法[J]. 西安交通大学学报, 2021, 55(11): 115-124.
WANG F P, CHEN P B, LIU W H, et al. Mixed noise-robust corner detection algorithm with nonlinear directional derivative[J]. Journal of Xi'an Jiaotong University, 2021, 55(11): 115-124.
谢永华, 陈庆为. 木材缺陷的阈值分割算法研究[J]. 森林工程, 2014, 30(2): 38-42.
XIE Y H, CHEN Q W. Study on wood defect image threshold segmentation algorithms[J]. Forest Engineering, 2014, 30(2): 38-42.
戴天虹, 吴以. 基于OTSU算法与数学形态学的木材缺陷图像分割[J]. 森林工程, 2014, 30(2): 52-55.
DAI T H, WU Y. Image segmentation of wood defect-based OTSU algorithm and mathematical morphology[J]. Forest Engineering, 2014, 30(2): 52-55.
Haralick R M, Shanmugam K, Dinstein I H. Textural features for image classification[C]. IEEE transactions on systems, man, and cybernetics, 1973: 610-621.
Kobayashi K, Hwang S W, Lee W H, et al. Texture analysis of stereograms of diffuse-porous hardwood: identification of wood species used in Tripitaka Koreana[J]. Journal of Wood Science, 2017, 63(4): 322-330.
Kobayashi K, Hwang S W, Okochi T, et al. Non-destructive method for wood identification using conventional X-ray computed tomography data[J]. Journal of Cultural Heritage, 2019, 38: 88-93.
Zamri M I P, Cordova F, Khairuddin A S M, et al. Tree species classification based on image analysis using Improved-Basic Gray Level Aura Matrix[J]. Computers and Electronics in Agriculture, 2016, 124: 227-233.
Ojala T, Pietikäinen M, Harwood D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1): 51-59.
SMITH A R. Color gamut transform pairs[J]. ACM Siggraph Computer Graphics, 1978, 12(3):12-19.
Blackmer T M, Schepers J S. Aerial photography to detect nitrogen stress in corn[J]. Journal of Plant Physiology, 1996, 148(3/4): 440-444.
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.
王辉. 基于灰度共生矩阵木材表面纹理模式识别方法的研究[D]. 哈尔滨: 东北林业大学, 2007.
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(3): 12-18.
XIAO Y Q, YANG H M, WANG K X, et al. Research progress of convolutional neural network in wood defect detection[J]. Chinese Journal of Wood Science and Technology, 2021, 35(3): 12-18.
Bogucki R, Cygan M, Khan C B, et al. Applying deep learning to right whale photo identification[J]. Conservation Biology, 2019, 33(3): 676-684.
何拓, 刘守佳, 陆杨, 等. 木材树种计算机视觉识别技术发展与应用[J]. 林业工程学报, 2021, 6(3): 18-27.
HE T, LIU S J, LU Y, et al. Advances and prospects of wood identification technology coupled with computer vision[J]. Journal of Forestry Engineering, 2021, 6(3): 18-27.
林启招, 孙永科, 何鑫, 等. 木材树种检索方法现状与智能检索初探[J]. 木材科学与技术, 2021, 35(2): 37-41.
LIN Q Z, SUN Y K, HE X, et al. Status review of retrieval methods for wood species and preliminary result of developing intelligent retrieval program[J]. Chinese Journal of Wood Science and Technology, 2021, 35(2): 37-41.
何拓, 高瑞清, 焦立超, 等. 世界木材标本馆现状与发展建议[J]. 木材工业, 2020, 34(3): 40-43, 55.
HE T, GAO R Q, JIAO L C, et al. Present status of global leading Xylaria and suggestions for Xylaria development in China[J]. China Wood Industry, 2020, 34(3): 40-43, 55.
Hwang S W, Sugiyama J. Computer vision-based wood identification and its expansion and contribution potentials in wood science: a review[J]. Plant Methods, 2021, 17(1): 47.
HWANG SW, KOBAYASHI K, ZHAI S, et al. Automated identification of Lauraceae by scale-invariant feature transform[J]. Journal of Wood Science, 2018, 64(2):69-77.
Lens F, Liang C, Guo Y H, et al. Computer-assisted timber identification based on features extracted from microscopic wood sections[J]. IAWA Journal / International Association of Wood Anatomists, 2020, 41(4): 1-21.
Ravindran P, Costa A, Soares R, et al. Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks[J]. Plant Methods, 2018, 14: 25.
HE T, LU Y, JIAO L, et al. Developing deep learning models to automate rosewood tree species identification for CITES designation and implementation[J]. Holzforschung, 2020, 74(12):1123-1133.
何拓, 焦立超, 郭娟, 等. 木材信息学：发展、应用与展望[J]. 木材科学与技术, 2021, 35(4): 15-24.
HE T, JIAO L C, GUO J, et al. Wood informatics: history of development, application, and prospective trend[J]. Chinese Journal of Wood Science and Technology, 2021, 35(4): 15-24.
Kurdthongmee W, Suwannarat K. An efficient algorithm to estimate the pith location on an untreated end face image of a rubberwood log taken with a normal camera[J]. European Journal of Wood and Wood Products, 2019, 77(5): 919-929.
杨攀, 郑积仕, 冯芝清, 等. 基于Mask R-CNN的密集木材检测分割方法[J]. 林业工程学报, 2022, 7(2): 135-142.
YANG P, ZHENG J S, FENG Z Q, et al. Research on detection and segmentation methods of dense-stacked logs using Mask R-CNN[J]. Journal of Forestry Engineering, 2022, 7(2): 135-142.
Thomas E. An artificial neural network for real-time hardwood lumber grading[J]. Computers and Electronics in Agriculture, 2017, 132: 71-75.
HU J F, SONG W L, ZHANG W, et al. Deep learning for use in lumber classification tasks[J]. Wood Science and Technology, 2019, 53(2): 505-517.
Bhandarkar S M, Luo X Z, Daniels R F, et al. Automated planning and optimization of lumber production using machine vision and computed tomography[J]. IEEE Transactions on Automation Science and Engineering, 2008, 5(4): 677-695.
张赛, 王应彪, 杨谭, 等. 基于改进LeNet-5模型的木材表面典型缺陷识别方法研究[J]. 木材科学与技术, 2021, 35(6): 31-37.
ZHANG S, WANG Y B, YANG T, et al. Detecting method of the wood surface defects based on modified LeNet-5 model[J]. Chinese Journal of Wood Science and Technology, 2021, 35(6): 31-37.
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.
郭慧, 盛振湘, 王霄, 等. 基于机器视觉的刨花板表面缺陷检测系统[J]. 木材工业, 2019, 33(3): 18-22.
GUO H, SHENG Z X, WANG X, et al. A system based on machine vision for detecting surface defects of particleboard[J]. China Wood Industry, 2019, 33(3): 18-22.
白雪冰, 许景涛, 宋恩来, 等. 3种不同模型对木材表面缺陷图像分割算法的比较[J]. 西部林业科学, 2016, 45(4): 27-34.
BAI X B, XU J T, SONG E L, et al. Contrast of three image segmentation algorithms for wood surface defects[J]. Journal of West China Forestry Science, 2016, 45(4): 27-34.
郭慧, 王霄, 刘传泽, 等. 基于灰度共生矩阵和分层聚类的刨花板表面图像缺陷提取方法[J]. 林业科学, 2018, 54(11): 111-120.
GUO H, WANG X, LIU C Z, et al. Research on defect extraction of particleboard surface images based on gray level co-occurrence matrix and hierarchical clustering[J]. Scientia Silvae Sinicae, 2018, 54(11): 111-120.
郭慧, 王霄, 刘传泽, 等. 人造板表面缺陷检测图像自适应快速阈值分割算法[J]. 林业科学, 2018, 54(11): 134-142.
GUO H, WANG X, LIU C Z, et al. Research on adaptive fast threshold segmentation algorithm for surface defect detection of wood-based panel[J]. Scientia Silvae Sinicae, 2018, 54(11): 134-142.
REN R X, HUNG T, TAN K C. A generic deep-learning-based approach for automated surface inspection[J]. IEEE Transactions on Cybernetics, 2018, 48(3): 929-940.
高琳明, 徐风, 李享, 等. 基于深度学习特征和非线性支持向量机的板材表面缺陷识别方法[J]. 林业工程学报, 2019, 4(4): 99-106.
GAO L M, XU F, LI X, et al. Board surface defects recognition method based on deep learning features and non-linear support vector machine[J]. Journal of Forestry Engineering, 2019, 4(4): 99-106.
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, 2020, 152: 107357.
李超, 吕宪伟, 涂文俊, 等. 基于计算机视觉的实木表面智能化分选系统设计[J]. 北京林业大学学报, 2016, 38(3): 102-109.
LI C, LYU X W, TU W J, et al. Design of an intelligent wood surface grading system based on computer vision[J]. Journal of Beijing Forestry University, 2016, 38(3): 102-109.
王锦亚, 李振业, 倪超. 基于机器视觉的实木地板在线分色识别算法[J]. 林业工程学报, 2021, 6(5): 135-139.
WANG J Y, LI Z Y, NI C. Machine vision algorithms of online color recognition and classification for solid wood flooring products[J]. Journal of Forestry Engineering, 2021, 6(5): 135-139.
刘红, 邓宸, 夏颖怡. 基于机器视觉的竹片颜色分选系统设计[J]. 装备制造技术, 2018(5): 145-147, 178.
LIU H, DENG C, XIA Y Y. Design of bamboo color sorting system based on machine vision[J]. Equipment Manufacturing Technology, 2018(5): 145-147, 178.
张豪, 赵小矛. 基于机器视觉的家具零件精度检测系统探究[J]. 家具与室内装饰, 2017(4): 86-88.
ZHANG H, ZHAO X M. Research on the precision monitoring system of furniture parts based on machine vision technology[J]. Furniture ＆ Interior Design, 2017(4): 86-88.
韩伟聪, 鲍光海. 基于机器视觉的竹材尺寸测量系统设计[J]. 中国测试, 2016, 42(7): 74-78.
HAN W C, BAO G H. Design of measurement system of bamboo size based on machine vision[J]. China Measurement ＆ Test, 2016, 42(7): 74-78.
邵帮丽, 朱寅, 朱润, 等. 一种面向智能家居设备控制的多模态人机智能交互方法[J]. 林业工程学报, 2021, 6(4): 190-196.
SHAO B L, ZHU Y, ZHU R, et al. Multi-modal man-machine intelligent interaction approach for smart home equipment control[J]. Journal of Forestry Engineering, 2021, 6(4): 190-196.
邓斌攸, 潘云峰, 池志强, 等. 基于机器视觉的家具板件尺寸在线测量系统[J]. 木材科学与技术, 2021, 35(2): 63-67.
DENG B Y, PAN Y F, CHI Z Q, et al. A machine vision system for measuring dimensions of furniture panels[J]. Chinese Journal of Wood Science and Technology, 2021, 35(2): 63-67.
钟世禄, 张辉亮. 面向智能制造的参数化三维设计在定制家居产品中的应用[J]. 木材科学与技术, 2021, 35(6): 20-25.
ZHONG S L, ZHANG H L. Application of parametric three-dimension design in custom intelligent manufacturing of interior furnishing products[J]. Chinese Journal of Wood Science and Technology, 2021, 35(6): 20-25.
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