Detecting internal defects in woods using intersection fitting

WEI Shuo; LI Guanghui; MA Jiahui

doi:10.11975/j.issn.1002-6819.202401162

Volume 40 Issue 23

Dec. 2024

Turn off MathJax

Article Contents

Abstract

References

Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE) > 2024 > 40(23): 267-273. > DOI: 10.11975/j.issn.1002-6819.202401162

WEI Shuo, LI Guanghui, MA Jiahui. Detecting internal defects in woods using intersection fitting[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(23): 267-273. DOI: 10.11975/j.issn.1002-6819.202401162

Citation:

PDF (1206 KB)

Detecting internal defects in woods using intersection fitting

School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

More Information

Received Date: January 19, 2024
Revised Date: August 04, 2024
Available Online: September 12, 2024

Graphical Abstract

Abstract

Abstract

Wood defects have significantly jeopardized the health of trees in the utility value of timber, leading to an immeasurable impact on ancient and venerable trees. Therefore, non-destructive testing (NDT) technology can play a crucial role in the efficient utilization of timber and the conservation of heritage woods. Among them, stress wave testing has been widely applied in recent years, due to its safety, portability, and adaptability to complex environments. However, the conventional stress wave NDT can often fail to consider the influence of wood anisotropy on wave propagation, thereby limiting the imaging accuracy and effectiveness in the detection of internal defects in tree trunks. Furthermore, stress wave data can be collected to uniformly distribute several sensors along the cross-section of the wood under test, in order to accurately map actual cavities. There is also some mismatch between the initial stress wave data visualization and the actual situation in the tree cross-section, due to the uneven speed of stress wave propagation that is caused by wood anisotropy. In this study, an approach was proposed to correct the collected stress wave velocity, and then normalize the corrected velocity using deviation rates. The imaging area was subdivided into multiple grid cells. The speed function of each stress wave ray was refitted, according to the intersection speeds of the rays. A stress wave propagation ray diagram was then drawn using the fitted ray speeds. The speeds of each grid cell were calculated in the imaging area. The nearest neighbor interpolation was used to realize some cells without data. Defect status was determined using the grid cell speeds. The internal defect images of the tree were reconstructed using image processing techniques. Five log samples were tested with the proportion of the defect area after image segmentation and the degree of overlap with the defect shape as evaluation criteria. The results indicate that the imaging algorithm achieved an overall average relative error, accuracy, precision, and recall rate of 8.25%, 93.19%, 80.37%, and 82.30%, respectively. The positions and sizes of defect areas were more consistent with the actual situation. The wave speed model improved the data processing of traditional ones. The robustness against crack interference was also enhanced to identify the crack regions. The findings can greatly contribute to the efficient utilization of timber. A theoretical basis can also provide for the conservation of heritage woods. However, some limitations still remained to detect the micro-cracks and decay on the tomography imaging using intersection fitting, indicating some improvement and optimization. Future research efforts can focus primarily on the robustness and imaging accuracy of tomography imaging using intersection fitting. A solid foundation can also be laid to develop three-dimensional imaging technology.
- non-destructive test,
- stress wave,
- tomography imaging,
- intersection fitting; wood; deviation rate

FullText(HTML)

References (31)

References

[1]	XU P, GUAN C, ZHANG H, et al. Application of nondestructive testing technologies in preserving historic trees and ancient timber structures in china[J]. Forests, 2021, 12(3): 318-333. doi: 10.3390/f12030318
[2]	WANG X. Recent advances in nondestructive evaluation of wood: in-forest wood quality assessments[J]. Forests, 2021, 12(7): 949-955. doi: 10.3390/f12070949
[3]	赵鹏,赵匀,陈广胜. 基于3D扫描技术的木材缺陷定量化分析[J]. 农业工程学报,2017,33(7):171-176. doi: 10.11975/j.issn.1002-6819.2017.07.022 ZHAO Peng, ZHAO Yun, CHEN Guangsheng. Quantitative analysis of wood defect based on 3D scanning technique[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2017, 33(7): 171-176. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2017.07.022
[4]	MORALES-CONDE M J, MACHADO J S. Evaluation of cross-sectional variation of timber bending modulus of elasticity by stress waves[J]. Construction and Building Materials, 2017, 134: 617-625. doi: 10.1016/j.conbuildmat.2016.12.188
[5]	HASELI M, LAYEGHI M, HOSSEINABADI H Z. Evaluation of modulus of elasticity of date palm sandwich panels using ultrasonic wave velocity and experimental models[J]. Measurement, 2020, 149: 107016. doi: 10.1016/j.measurement.2019.107016
[6]	DEL MENEZZI C H S, AMORIM M R S, COSTA M A, et al. Evaluation of thermally modified wood by means of stress wave and ultrasound nondestructive methods[J]. Materials Science, 2014, 20(1): 61-66.
[7]	李光辉,刘敏,徐汇,等. 探地雷达偏移成像检测树干空洞[J]. 农业工程学报,2021,37(15):154-160. doi: 10.11975/j.issn.1002-6819.2021.15.019 LI Guanghui, LIU Min, XU Hui, et al. Tree trunk cavity detection using ground-penetrating radar migration imaging[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(15): 154-160. (in Chinesewith English abstract) doi: 10.11975/j.issn.1002-6819.2021.15.019
[8]	吴方明. 活立木微波无损检测技术研究进展[J]. 林业机械与木工设备,2018,46(12):9-14. doi: 10.3969/j.issn.2095-2953.2018.12.002 WU Fangming. Research progress of microwave nondestructive testing technology for standing trees[J]. Forestry Machinery & Woodworking Equipment, 2018, 46(12): 9-14. (in Chinese with English abstract) doi: 10.3969/j.issn.2095-2953.2018.12.002
[9]	AYANLEYE S, NASIR V, AVRAMIDIS S, et al. Effect of wood surface roughness on prediction of structural timber properties by infrared spectroscopy using ANFIS, ANN and PLS regression[J]. European Journal of Wood and Wood Products, 2021, 79: 101-115. doi: 10.1007/s00107-020-01621-x
[10]	TONANNAVAR S, SHIVAKUMAR N D, SIMHA K R Y, et al. Quality assessment of artocarpus heterophyllus log using nondestructive evaluation techniques[J]. Journal of Nondestructive Evaluation, 2021, 40(2): 55-66. doi: 10.1007/s10921-021-00787-5
[11]	BERTHOLF L D. Use of elementary stress wave theory for prediction of dynamic strain in wood[M]. Technical Extension Service. Pullman: Washington State University, 1965.
[12]	杨学春,王立海. 原木内部腐朽应力波二维图像的获取与分析[J]. 林业科学,2007,43(11):93-97. doi: 10.3321/j.issn:1001-7488.2007.11.017 YANG Xuechun, WANG Lihai. Gain and analysis of two-dimensional images of interior decay of logs with stress wave method[J]. Scientia Silvae Sinicae, 2007, 43(11): 93-97. (in Chinese with English abstract) doi: 10.3321/j.issn:1001-7488.2007.11.017
[13]	DU X, FENG H, HU M, et al. Three-dimensional stress wave imaging of wood internal defects using TK riging method[J]. Computers and Electronics in Agriculture, 2018, 148: 63-71. doi: 10.1016/j.compag.2018.03.005
[14]	刘丰禄,张厚江,王喜平,等. 应力波在落叶松活立木中传播影响因素数值模拟[J]. 农业机械学报,2020,51(2):203-211. doi: 10.6041/j.issn.1000-1298.2020.02.022 LIU Fenglu, ZHANG Houjiang, WANG Xiping, et al. Numerical simulation of influence factors on stress wave propagation in larch standing trees[J]. Transactions of the Chinese Society for Agricultural Machinery, 2020, 51(2): 203-211. (in Chinese with English abstract) doi: 10.6041/j.issn.1000-1298.2020.02.022
[15]	ESPINOSA L, PRIETO F, BRANCHERIAU L, et al. Quantitative parametric imaging by ultrasound computed tomography of trees under anisotropic conditions: Numerical case study[J]. Ultrasonics, 2020, 102: 106060. doi: 10.1016/j.ultras.2019.106060
[16]	LI G, WANG X, FENG H, et al. Analysis of wave velocity patterns in black cherry trees and its effect on internal decay detection[J]. Computers and Electronics in Agriculture, 2014, 104: 32-39. doi: 10.1016/j.compag.2014.03.008
[17]	孙圆,夏庆哲,温小荣,等. 杨树人工林无损年轮计量特征气象响应分析[J]. 农业工程学报,2023,39(15):133-143. doi: 10.11975/j.issn.1002-6819.202304050 SUN Yuan, XIA Qingzhe, WEN Xiaorong, et al. Meteorological response analysis of the nondestructive tree ring characteristics of poplar plantation[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions ofthe CSAE), 2023, 39(15): 133-143. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.202304050
[18]	CHENG L, DAI J, YANG Z, et al. Variation of larch wood property indexes based on nondestructive testing data[J]. Bioresources, 2020, 15(2): 2906-2923. doi: 10.15376/biores.15.2.2906-2923
[19]	JASKOWSKA J, PRZESMYCKA E. Semi-destructive and non-destructive tests of timber structure of various moisture contents[J]. Materials, 2020, 14(1): 96-119. doi: 10.3390/ma14010096
[20]	WEI X, SUN L, ZHOU H, et al. Propagation velocity model of stress waves in larch wood (Larix gmelinii) three-dimensional space with different moisture contents[J]. BioResources, 2020, 15(3): 6680-6695. doi: 10.15376/biores.15.3.6680-6695
[21]	MA S, REN S, CHEN Z, et al. Wooden beam damage evaluation under bending loading based on the integration of acoustic emission and principal component analysis[J]. Measurement, 2023, 222: 113569. doi: 10.1016/j.measurement.2023.113569
[22]	YE R, PEI Y, WANG W, et al. Scientific computational visual analysis of wood internal defects detection in view of tomography image reconstruction algorithm[J]. Mobile Information Systems, 2022, 2022: 6091352.
[23]	顾宝兴,刘钦,田光兆,等. 基于改进YOLOv3的果树树干识别和定位[J]. 农业工程学报,2022,38(6):122-129. doi: 10.11975/j.issn.1002-6819.2022.06.014 GU Baoxing, LIU Qin, TIAN Guangzhao, et al. Recognizing and locating the trunk of a fruit tree using improved YOLOv3[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2022, 38(6): 122-129. (in Chinese with English abstract) doi: 10.11975/j.issn.1002-6819.2022.06.014
[24]	刘涛,李光辉. 基于射线分割的林木应力波断层成像算法[J]. 林业科学,2021,57(9):181-192. doi: 10.11707/j.1001-7488.20210918 LIU Tao, LI Guanghui. Stress wave tomography imaging algorithm based on ray segmentation for nondestructive testing of wood[J]. Scientia Silvae Sinicae, 2021, 57(9): 181-192. (in Chinese with English abstract) doi: 10.11707/j.1001-7488.20210918
[25]	NASIR V, NOURIAN S, AVRAMIDIS S, et al. Stress wave evaluation for predicting the properties of thermally modified wood using neuro-fuzzy and neural network modeling[J]. Holzforschung, 2019, 73(9): 827-838. doi: 10.1515/hf-2018-0289
[26]	GAO M, QI D, MU H, et al. A transfer residual neural network based on ResNet-34 for detection of wood knot defects[J]. Forests, 2021, 12(2): 212-228. doi: 10.3390/f12020212
[27]	DU X, LI J, FENG H, et al. Image reconstruction of internal defects in wood based on segmented propagation rays of stress waves[J]. Applied Sciences, 2018, 8(10): 1778-1797. doi: 10.3390/app8101778
[28]	ESPINOSA L, ARCINIEGAS A, CORTES Y, et al. Automatic segmentation of acoustic tomography images for the measurement of wood decay[J]. Wood Science and Technology, 2017, 51: 69-84. doi: 10.1007/s00226-016-0878-1
[29]	DIKRALLAH A, HAKAM A, BRANCHERIAU L, et al. Experimental analysis of acoustic anisotropy of wood by using guided waves[C]//International conference on integrated approach to wood structure, behavior and application. Italy: Florence, 2006: 149-154.
[30]	ARCINIEGAS A, BRANCHERIAU L, LASAYGUES P. Tomography in standing trees: Revisiting the determination of acoustic wave velocity[J]. Annals of Forest Science, 2015, 72: 685-691. doi: 10.1007/s13595-014-0416-y
[31]	LIU F, ZHANG H, JIANG F, et al. Variations in orthotropic elastic constants of green Chinese Larch from pith to sapwood[J]. Forests, 2019, 10(5): 456-473. doi: 10.3390/f10050456

[1]	CAI Jianrong, LIANG Xiaoxiang, XU Qian, XIA Zhongyan, SUN Li, MA Lixin. Detecting volumetric edible rate of thick-skinned citrus using X-ray three-dimensional reconstruction[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2024, 40(1): 293-300. DOI: 10.11975/j.issn.1002-6819.202309084
[2]	Sheng Feng, Wen Ding, Xiong Yiwei, Wang Kang. In-situ monitoring of preferential soil water flow with electrical resistivity tomography technology[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(8): 117-124. DOI: 10.11975/j.issn.1002-6819.2021.08.013
[3]	Ta Na, Wu Shiliu, Ma Wenjuan, Chen Bin, Zhu Yingkai. Peak-fitting based prediction of soil temperature according to soil moisture content in solar greenhouse[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(20): 204-210. DOI: 10.3969/j.issn.1002-6819.2014.20.025
[4]	Li Xingshu, Cui Meng, Yang Jianxiong, Han Wenting, Xiong Xiufang. Tomographic image reconstruction of plant single root by electrical impedance tomography[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(16): 173-180. DOI: 10.3969/j.issn.1002-6819.2014.16.023
[5]	Wu Wenbin, Yang Peng, Tang Huajun, Zhou Qingbo, Shibasaki Ryosuke, Zhang Li. Comparison of two fitting methods of NDVI time series datasets[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(11): 183-188.
[6]	Zhang Lin, Fan Xingke, Wu Pute, Niu Wenquan, Yu Liming. Calculation of flow deviation rate of drip irrigation system taking three deviation rates into account on uniform slopes[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2009, 25(4): 7-14.
[7]	Wu Hao, Qiu Baijing, Tang Bomin. Fitting method of pesticide deposition curves[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2008, 24(10): 118-121.
[8]	Zhang Guoxiang. Calculating the total flow deviation rate of drip-irrigation system based on three deviation rates[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2006, 22(11): 27-29.
[9]	Hu Kun, Zhang Jianian. Selection of fitting models of adsorption and desorption isotherms of rice and optimization of their parameters[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2006, 22(1): 153-156.
[10]	Niu Wenquan, Wu Pute, Fan Xingke. Method for calculating integrated flux deviation rate of micro-irrigation system[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2004, 20(6): 85-88.