张智光. 可再生资源型企业的绿色特征与绿色战略类型体系研究——以林业企业为例[J]. 南京林业大学学报(自然科学版), 2020, 44(6): 1-8.
易龙生, 米宏成, 吴倩, 等. 中国尾矿资源综合利用现状[J]. 矿产保护与利用, 2020, 40(3): 79-84.
马永康, 刘华, 凌成星, 等. 基于改进YOLOv5的红树林单木目标检测研究[J]. 激光与光电子学进展, 2022, 59(18): 436-446.
YANG Y T, WANG HH, JIANG D, et al. Surface Detection of Solid Wood Defects Based on SSD Improved with ResNet[J]. Forests, 2021, 12(10): 1419-1430. doi: 10.3390/f12101419
SOGE A O, POPOOLA O I, ADETOYINBO A A. Detection of Wood Decay and Cavities in Living Trees: A Review[J]. Canadian Journal of Forest Research, 2021, 51(7): 937-947. doi: 10.1139/cjfr-2020-0340
DING F L, ZHUANG Z L, LIU Y, et al. Detecting Defects on Solid Wood Panels Based on an Improved SSD Algorithm[J]. Sensors, 2020, 20(18): 5315-5332. doi: 10.3390/s20185315
IBRAHIM E A B, HASHIM U R, SALAHUDDIN L, et al. Evaluation of Texture Feature Based on Basic Local Binary Pattern for Wood Defect Classification[J]. International Journal of Advances in Intelligent Informatics, 2021, 7(1): 26-36. doi: 10.26555/ijain.v7i1.393
CHUN T H, HASHIM U R, AHMAD S, et al. Identification of Wood Defect Using Pattern Recognition Technique[J]. International Journal of Advances in Intelligent Informatics, 2021, 7(2): 163. doi: 10.26555/ijain.v7i2.588
PAN S, WANG K Q, CHEN J H, et al. Larch Wood Defect Definition and Microscopic Inversion Analysis Using the ELM Near-Infrared Spectrum Optimization along with WOA-SVM[J]. BioResources, 2021, 17(1): 682-698.
Mohsin M, Balogun O S, Haataja K, et al. Defect Detection Using Deep Neural Networks and Algorithms: A Survey[J]. Solid State Technology, 2023, 66(1): 23-39.
RIANA D, RAHAYU S, HASAN M, et al. Comparison of Segmentation and Identification of Swietenia Mahagoni Wood Defects with Augmentation Images[J]. Heliyon, 2021, 7(6): e07417. doi: 10.1016/j.heliyon.2021.e07417
HU K, WANG B J, SHEN Y, et al. Defect Identification Method for Poplar Veneer Based on Progressive Growing Generated Adversarial Network and MASK R-CNN Model[J]. BioResources, 2020, 15(2): 3041-3052. doi: 10.15376/biores.15.2.3041-3052
CHO P, WOOD A, MAHALINGAM K, et al. Defect Detection in Atomic Resolution Transmission Electron Microscopy Images Using Machine Learning[J]. Mathematics, 2021, 9(11): 1209-1225. doi: 10.3390/math9111209
CHEN L C, PARDESHI M S, LO W T, et al. Edge-Glued Wooden Panel Defect Detection Using Deep Learning[J]. Wood Science and Technology, 2022, 56(2): 477-507. doi: 10.1007/s00226-021-01316-3
陶显, 侯伟, 徐德. 基于深度学习的表面缺陷检测方法综述[J]. 自动化学报, 2021, 47(5): 1017-1034.
GAO M Y, QI D W, MU H B, et al. A Transfer Residual Neural Network Based on ResNet-34 for Detection of Wood Knot Defects[J]. Forests, 2021, 12(2): 212-228.
张家瑜, 周迪斌, 魏东亮, 等. 基于双线性CNN与DenseBlock的导光板标记线缺陷检测[J]. 计算机系统应用, 2020, 29(7): 152-159.
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.
戴天虹, 谢千程, 黄建平, 等. 一种弱监督细粒度深度网络的木材分类方法[J]. 西南大学学报(自然科学版), 2022, 44(10): 161-172. doi: 10.13718/j.cnki.xdzk.2022.10.017
REZAUR RAHMAN CHOWDHURY F A, WANG Q, MORENO I L, et al. Attention-Based Models for Text-Dependent Speaker Verification[C] //2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Calgary: IEEE, 2018: 5359-5363.
YANG Y T, ZHOU X L, LIU Y, et al. Wood Defect Detection Based on Depth Extreme Learning Machine[J]. Applied Sciences, 2020, 10(21): 7488-7502.
CHEN L C, PARDESHI M S, LO W T, et al. Edge-Glued Wooden Panel Defect Detection Using Deep Learning[J]. Wood Science and Technology, 2022, 56(2): 477-507.
SHI J H, LI Z Y, ZHU TT, et al. Defect Detection of Industry Wood Veneer Based on NAS and Multi-Channel Mask R-CNN[J]. Sensors, 2020, 20(16): 4398-4415.