[1] 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 Wei, SUN Liping. Wood defect detection and classification by fusion feature and support vector machine [J]. Journal of Northeast Forestry University, 2019, 47(6): 70 − 73.
[2] 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 Ying, ZHOU Xiaolin, HU Zhongkang, et al. Wood defect recognition based on optimized convolution neural network algorithm [J]. Journal of Forestry Engineering, 2019, 4(1): 115 − 120.
[3] 凌嘉欣, 谢永华. 残差神经网络模型在木质板材缺陷分类中的应用[J]. 东北林业大学学报, 2021, 49(8): 111 − 116.

LING Jiaxin, XIE Yonghua. Residual neural network model in wood plate defect classification [J]. Journal of Northeast Forestry University, 2021, 49(8): 111 − 116.
[4] WANG Xuejuan, WU Shuhang, LIU Yunpeng. Detecting wood surface defects with fusion algorithm of visual saliency and local threshold segmentation [C/OL]// YU Hui, DONG Junyu. Ninth International Conference on Graphic and Image Processing (Icgip 2017), 2018: 10615[2023-05-02]. https://doi.org/10.1117/12.2302944.
[5] LUO Wei, SUN Liping. An improved binarization algorithm of wood image defect segmentation based on non-uniform background [J]. Journal of Forestry Research, 2019, 30(4): 1527 − 1533.
[6] 胡笑天, 王克俭, 王超, 等. 一种基于改进SSD的原木端面识别方法[J]. 林业工程学报, 2023, 8(1): 141 − 149.

HU Xiaotian, WANG Kejian, WANG Chao, et al. Development of log end face recognition method based on improved SSD [J]. Journal of Forestry Engineering, 2023, 8(1): 141 − 149.
[7] 余平平, 林耀海, 赖云锋, 等. 融合BiFPN和YOLOv5s的密集型原木端面检测方法[J]. 林业工程学报, 2023, 8(1): 126 − 134.

YU Pingping, LIN Yaohai, LAI Yunfeng, et al. Dense log end face detection method using the hybrid of BiFPN and YOLOv5s [J]. Journal of Forestry Engineering, 2023, 8(1): 126 − 134.
[8] 郑积仕, 张世文, 杨攀, 等. 基于深度学习与深度信息的原木材积检测方法[J]. 林业工程学报, 2023, 8(1): 135 − 140.

ZHENG Jishi, ZHANG Shiwen, YANG Pan, et al. Log volume detection method based on deep learning and depth information [J]. Journal of Forestry Engineering, 2023, 8(1): 135 − 140.
[9] NI Chao, LI Zhenye, ZHANG Xiong, et al. Online sorting of the film on cotton based on deep learning and hyperspectral imaging [J]. IEEE Access, 2020, 8: 93028 − 93038.
[10] LUO Qiwu, FANG Xiaoxin, SU Jiaojiao, et al. Automated visual defect classification for flat steel surface: a survey [J]. Ieee Transactions on Instrumentation and Measurement, 2020, 69(12): 9329 − 9349.
[11] 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 Hui, WANG Xiao, LIU Chuanze, 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.
[12] 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 Hui, WANG Xiao, LIU Chuanze, 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.
[13] 刘传泽, 陈龙现, 刘大伟, 等. 基于剪枝决策树的人造板表面缺陷识别[J]. 计算机系统应用, 2018, 27(11): 168 − 173.

LIU Chuanze, CHEN Longxian, LIU Dawei, et al. Defect recognition of wood-based panel surface using pruning decision tree [J]. Computer Systems &Applications, 2018, 27(11): 168 − 173.
[14] 刘传泽, 罗瑞, 陈龙现, 等. 基于区域筛选分割和随机森林的人造板表面缺陷识别[J]. 制造业自动化, 2018, 40(9): 9 − 13.

LIU Chuanze, LUO Rui, CHEN Longxian, et al. Surface defect recognition of wood-based panel based on regional screening and segmentation and random forest [J]. Manufacturing Automation, 2018, 40(9): 9 − 13.
[15] LIU Wei, ANGUELOV D, ERHAN D, et al. SSD: single shot multibox detector [M]// LEIBE B, MATAS J, SEBE N, et al. Computer Vision-ECCV 2016. Amsterdam: Springer Cham, 2016: 21 − 37.
[16] GIRSHICK R. Fast R-CNN [C]// IEEE Computer Society. 2015 Ieee International Conference on Computer Vision (ICCV), Los Alamitos: IEEE, 2015: 1440 − 1448.
[17] REN Shaoqing, HE Kaiming, GIRSHICK R, et al. Faster R-CNN: towards real-time object detection with region proposal networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017, 39(6): 1137 − 1149.
[18] REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once: unified, real-time object detection [C]. IEEE Computer Society. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas: IEEE, 2016: 779 − 788.
[19] ZHU Xingkui, LYU Shuchang, WANG Xu, et al. TPH-YOLOv5: improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios [C]. IEEE Computer Society. 2021 IEEE/Cvf International Conference on Computer Vision Workshops (ICCVW 2021), Los Alamitos: IEEE, 2021: 2778 − 2788.
[20] 宋小燕, 白福忠, 武建新, 等. 应用灰度直方图特征识别木材表面节子缺陷[J]. 激光与光电子学进展, 2015, 52(3): 205 − 210.

SONG Xiaoyan, BAI Fuzhong, WU Jianxin, et al. Wood knot defects recognition with gray-scale histogram features [J]. Laser &Optoelectronics Progress, 2015, 52(3): 205 − 210.
[21] SANDLER M, HOWARD A, ZHU M, et al. MobileNetV2: inverted residuals and linear bottlenecks [J]. IEEE Computer Society. 2018 IEEE/Cvf Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City: IEEE, 2018: 4510 − 4520.