1.东北林业大学工程技术学院，黑龙江哈尔滨 150040
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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
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