AUTOMATED VISION INSPECTION OF TIMBER SURFACE DEFECT: A REVIEW
DOI:
https://doi.org/10.11113/jt.v77.6562Keywords:
Automated vision inspection, defect detection, wood inspection, timber defect detection, non-destructive testingAbstract
Automated inspection has proven to be of great importance in increasing the quality of timber products, optimising raw material resources, increasing productivity as well as reducing error related to human labour. This paper reviews automated inspection of timber surface defects with a special focus on vision inspection. Previous works on sensors utilised are presented and can be used as a reference for future researchers. General approaches to solving the problem of wood surface defect detection can be categorised into segmentation and non-segmenting approaches. The weaknesses and strengths of each approach are discussed along with feature extraction techniques and classifiers implemented in timber surface defect detection. Furthermore, insights into the practicality of implementing automated vision inspection of timber defects were also discussed. This paper shall benefit researchers and practitioners in understanding different approaches, sensors, feature extraction techniques as well as classifiers that have been implemented in automated inspection of timber surface defects, thus providing some direction for future research.
References
Buehlmann, U., Edward Thomas, R., and Thomas, R. E. 2002. Impact of Human Error on Lumber Yield in Rough Mills. Robotics and Computer-Integrated Manufacturing. 18(3-4): 197-203.
Huber, H. A., Mcmillin, C. W., and Mckinney, J. P. 1985. Lumber Defect Detection Abilities of Furniture Rough Mill Employees. Forest Products Journal. 35(11): 79-82.
Buehlmann, U., and Thomas, R. E. 2006. Relationship Between Lumber Yield and Board Marker Accuracy in Rip-First Rough Mills. Holz als Roh- und Werkstoff. 65(1): 43-48.
Kim, C.-W., and Koivo, A. J. 1994. Hierarchical Classification of Surface Defects on Dusty Wood Boards. Pattern Recognition Letters. 15(7): 713-721.
Kline, D. E. E., Surak, C., and Araman, P. A. 2003. Automated Hardwood Lumber Grading Utilizing a Multiple Sensor Machine Vision Technology. Computers and Electronics in Agriculture. 41(1-3): 139-155.
Yuce, B., and Mastrocinque, E. 2014. Neural Network Design and Feature Selection Using Principal Component Analysis and Taguchi Method for Identifying Wood Veneer Defects. Production & Manufacturing Research. 2(1): 291-308.
Chen, L., Wang, K., Xie, Y., and Wang, J. 2014. The Segmentation of Timber Defects Based on Color and the Mathematical Morphology. Optik. 125(3): 965-967.
Mulaveesala, R., Nagarjuna, V. P., Ravi, D., and Amarnath, M. 2012. Thermal Wave Imaging Techniques for Inspection of Plywood Materials. SPIE Defense, Security, and Sensing. 83540. International Society for Optics and Photonic.
Todoroki, C. L. L., Lowell, E. C. C., and Dykstra, D. 2010. Automated Knot Detection with Visual Post-Processing of Douglas-Fir Veneer Images. Computers and Electronics in Agriculture. 70(1): 163-171.
Li, M.-X., and Wu, C.-D. 2009. A VPRS and NN Method for Wood Veneer Surface Inspection. 2009 International Workshop on Intelligent Systems and Applications. 1-4. IEEE.
Castellani, M., and Rowlands, H. 2009. Evolutionary Artificial Neural Network Design and Training for Wood Veneer Classification. Engineering Applications of Artificial Intelligence. 22(4-5): 732-741.
Packianather, M. S., Drake, P. R., and Pham, D. T. 2008. Feature Selection Method for Neural Network for the Classification of Wood Veneer Defects. 2008 World Automation Congress. 790-795.
Li, M., Wu, C., Zhang, Y., and Yue, Y. 2006. An Improved BP Network Classifier Based on VPRS Feature Reduction. WCICA 2006 Sixth World Congress on Intelligent Control and Automation. 9677-9680.
Packianather, M. S., and Drake, P. R. 2005. Comparison of Neural and Minimum Distance Classifiers in Wood Veneer Defect Identification. Proceedings of the Institution of Mechanical Engineers Part B-Journal of Engineering Manufacture. 219(11): 831-841.
Funck, J. . J., Zhong, Y., Butler, D. . DA, Brunner, C. ., and Forrer, J. . 2003. Image segmentation algorithms applied to wood defect detection. Computers and Electronics in Agriculture. 41(1-3): 157–179.
Radovan, S., George, P., Panagiotis, M., Manos, G., Robert, A., and Igor, D. 2001. An Approach for Automated Inspection of Wood Boards. International Conference on Image Processing. 798-801. IEEE.
Nordmark, U. 2002. Knot Identification from CT Images of Young Pinus Sylvestris Sawlogs Using Artificial Neural Networks. Scandinavian Journal of Forest Research. 17(1): 72-78.
Rinnhofer, A., Jakob, G., Deutschl, E., Benesova, W., Andreu, J. P., Parziale, G., and Niel, A. 2005. A Multi-sensor System for Texture Based High-Speed Hardwood Lumber Inspection. Electronic Imaging 2005. 34-43. International Society for Optics and Photonics.
Sarigul, E., Abbott, A. L. L., and Schmoldt, D. L. 2003. Rule-driven Defect Detection in CT Images of Hardwood Logs. Computers and Electronics in Agriculture. 41(1-3): 101-119.
Thomas, L., Mili, L., Thomas, E., and Shaffer, C. A. 2006. Defect Detection on Hardwood Logs Using Laser Scanning. Wood and Fiber Science. 38(4): 682-695.
Bhandarkar, S. M., Luo, X., Daniels, R., and Tollner, E. W. 2006. A Novel Feature-based Tracking Approach to the Detection, Localization, and 3-D Reconstruction of Internal Defects in Hardwood Logs Using Computer Tomography. Pattern Analysis and Applications. 9(2-3): 155-175.
Rojas, G., Condal, A., Beauregard, R., Verret, D., and Hernandez, R. E. 2006. Identification of Internal Defect of Sugar Maple Logs from CT Images Using Supervised Classification Methods. Holz Als Roh-Und Werkstoff. 64(4): 295-303.
Qi, D., Yu, L., and Feng, X. 2008. A Detection Method on Wood Defects of CT Image Using Multifractal Spectrum Based on Fractal Brownian Motion. IEEE International Conference on Automation and Logistics. 1539-1544.
Halabe, U. B., Agrawal, S., and Gopalakrishnan, B. 2009. Nondestructive Evaluation of Wooden Logs Using Ground Penetrating Radar. Nondestructive Testing and Evaluation. 24(4): 329-346.
Gu, H., Yu, L., and Wang, Y. 2010. CT Number in Wood Physical Properties Prediction Based on Computed Tomography Technology. 2010 IEEE International Conference on Automation and Logistics, ICAL 2010. 279-284.
Dawei, Q. D. Q., Peng, Z. P. Z., Xuefei, Z. X. Z., and Xuejing, J. X. J. 2010. Edge Detection of Wood Defects in X-Ray Wood Image Using Neural Network and Mathematical Morphology. Control Conference (CCC), 2010 29th Chinese. IEEE.
Xu, H. X. H., Wang, L. W. L., and Ni, S. N. S. 2010. Application of Artificial Neural Network to Nondestructive Testing of Internal Wood Defects Based on the Intrinsic Frequencies. 2010 International Conference on System Science, Engineering Design and Manufacturing Informatization. 1: 207-210.
Sharma, S. K., and Shukla, S. R. 2012. Properties Evaluation and Defects Detection in Timbers by Ultrasonic Non-Destructive Technique. Journal of the Indian Academy of Wood Science. 9(1): 66-71.
Johansson, E., Johansson, D., Skog, J., and Fredriksson, M. 2013. Automated Knot Detection for High Speed Computed Tomography on Pinus Sylvestris L. and Picea Abies (L.) Karst. Using Ellipse Fitting in Concentric Surfaces. Computers and Electronics in Agriculture. 96: 238-245.
Gao, S., Wang, N., Wang, L., and Han, J. 2014. Application of an Ultrasonic Wave Propagation Field in the Quantitative Identification of Cavity Defect of Log Disc. Computers and Electronics in Agriculture. 108: 123-129.
Weidenhiller, A., and Denzler, J. K. 2014. On the Suitability of Colour and Texture Analysis for Detecting the Presence of Bark on a Log. Computers and Electronics in Agriculture. 106: 42-48.
Wang, X., Liang, D., and Deng, W. 2010. Surface Grading of Bamboo Strips Using Multi-Scale Color Texture Features in Eigenspace. Computers and Electronics in Agriculture. 73(1): 91-98.
Kyllonen, J., and Pietikainen, M. 2000. Visual Inspection of Parquet Slabs by Combining Color and Texture. Workshop on Machine Vision Applications. 187-192.
Polzleitner, W., and Schwingshakl, G. 2004. Real-time Color-Based Texture Analysis for Sophisticated Defect Detection on Wooden Surfaces. D. P. Casasent, E. L. Hall, & J. Roning (Eds.). Intelligent Robots and Computer Vision Xxii: Algorithms, Techniques, and Active Vision. 5608: 54-69.
Rozman, D., Brezak, M., and Petrovic, I. 2006. Parquet Sorting and Grading Based on Color and Texture Analyses. IEEE International Symposium on Industrial Electronics. 655-660. IEEE.
Bianconi, F., Fernández, A., González, E., Saetta, S. A., Fernandez, A., and Gonzalez, E. 2013. Performance Analysis of Colour Descriptors for Parquet Sorting. Expert Systems with Applications. 40(5): 1636-1644.
Piuri, V., Scotti, F., and Roveri, M. 2005. Visual Inspection of Particle Boards for Quality Assessment. IEEE International Conference on Image Processing 2005. III-521.
Gamassi, M., Piuri, V., Scotti, F., and Roveri, M. 2006. Genetic Techniques for Pattern Extraction in Particle Boards Images. IEEE International Conference on Computational Intelligence for Measurement Systems and Applications. 12-14.
Santiago, A. J. S., Yuste, A. J., Expoito, J. E. M., Galan, S. G., Prado, R. P., Maqueira, J. M., Garcia Galan, S. 2012. Real-time Image Texture Analysis in Quality Management Using Grid Computing: An Application to the MDF Manufacturing Industry. International Journal of Advanced Manufacturing Technology. 58(9-12): 1217-1225.
Wooten, J. R., Filip To, S. D., Igathinathane, C., and Pordesimo, L. O. 2011. Discrimination of Bark from Wood Chips Through Texture Analysis by Image Processing. Computers and Electronics in Agriculture. 79(1): 13-19.
Niemz, P., Kucera, L. J., Schob, M., and Scheffler, M. 1999. Possibility of Defects Detection in Wood with Ultrasound. Holz Als Roh-Und Werkstoff. 57(2): 96-102.
Wyckhuyse, A., and Maldague, X. 2001. A Study Of Wood Inspection by Infrared Thermography, Part II: Thermography for Wood Defects Detection. Research in Nondestructive Evaluation. 13(1): 13-21.
Pastorino, M., Salvade, A., Monleone, R., Bartesaghi, T., Bozza, G., Randazzo, A., and Ieee. 2007. Detection of Defects in Wood Slabs by Using a Microwave Imaging Technique. IEEE Instrumentation & Measurement Technology Conference. 1-6. IEEE.
Choi, F. C., Li, J., Samali, B., and Crews, K. 2007. Application of Modal-based Damage-detection Method to Locate And Evaluate Damage In Timber Beams. Journal of Wood Science. 53(5): 394-400.
Hu, C., Xiao, M., Zhou, H., Wen, W., and Yun, H. 2011. Damage Detection of Wood Beams Using the Differences In Local Modal Flexibility. Journal of Wood Science. 57(6): 479-483.
Longuetaud, F., Mothe, F., Kerautret, B., Krähenbühl, A., Hory, L., Leban, J. M., and Debled-Rennesson, I. 2012. Automatic Knot Detection and Measurements from X-Ray CT Images of Wood: A Review and Validation of an Improved Algorithm on Softwood Samples. Computers and Electronics in Agriculture. 85: 77-89.
Patricio, M. A. A., and Maravall, D. 2007. A Novel Generalization of the Gray-Scale Histogram and Its Application to the Automated Visual Measurement and Inspection of Wooden Pallets. Image and Vision Computing. 25(6): 805-816.
Conners, R. W., McMillin, C. W., Lin, K., and Vasquez-Espinosa, R. E. 1983. Identifying and Locating Surface Defects in Wood: Part of an Automated Lumber Processing System. IEEE Transactions on Pattern Analysis and Machine Intelligence. 5(6): 573-83.
Koivo, A., and Kim, C. 1988. Parameter Estimation of CAR Models for Classifying Wood Boards. Proceedings of the 1988 IEEE International Conference on Systems, Man, and Cybernetics. 2(9): 1376-1379.
Cho, T.-H., Conners, R. W., Araman, P. A., Service, U. S. F., Forest, B., and Tech, V. 1990. A Computer Vision System for Analyzing Images of Rough Hardwood Lumber. 10th International Conference on Pattern Recognition. 1: 726-728.
Cho, T., and Conners, R. 1991. A Neural Network Approach to Machine Vision Systems for Automated Industrial Inspection. IJCNN-91-Seattle International Joint Conference on Neural Networks. 3-8.
Conners, R., Cho, T., Ng, C., and Drayer, T. 1992. A machine vision system forautomatically grading hardwood lumber. Industrial Metrology. 2(1992): 317–342.
Astrand, E., Astrom, A., and Ã…strand, E. 1994. A Single Chip Multi-Function Sensor System for Wood Inspection. 2th IAPR International Conference on Pattern Recognition. 300-304.
Hagman, O. 1997. Multivariate Prediction of Wood Surface Features Using an Imaging Spectrograph. Holz als Roh-und Werkstoff. 55(6): 377-382.
Estevez, P., and Fernandez, M. 1999. Selection of Features for the Classification of Wood Board Defects. Ninth International Conference on Artificial Neural Networks. 347-352.
Niskanen, M., Silvén, O., and Kauppinen, H. 2001. Color and Texture Based Wood Inspection with Non-Supervised Clustering. Proceedings of the Scandinavian Conference On Image Analysis. 336-342.
Francini, F., Longobardi, G., Sansoni, P., Euzzor, S., and Ciamberlini, C. 2002. Identification of Timber Deformations. Journal of Optics a-Pure and Applied Optics. 4(6): S406-S412.
Hu, C. S., Tanaka, C., and Ohtani, T. 2003. Locating and Identifying Splits and Holes on Sugi by the Laser Displacement Sensor. Journal of Wood Science. 49(6): 492-498.
Lee, S.-M. M., Abbott, A. L. L., and Schmoldt, D. L. 2003. Surface Shape Analysis of Rough Lumber for Wane Detection. Computers and Electronics in Agriculture. 41(1-3): 121-137.
Estevez, P. A., Perez, C. A., and Goles, E. 2003. Genetic Input Selection to a Neural Classifier for Defect Classification Of Radiata Pine Boards. Forest Products Journal. 53(9468): 87-94.
Silven, O., Niskanen, M., and Kauppinen, H. 2003. Wood Inspection with Non-Supervised Clustering. Machine Vision and Applications. 13(5-6): 275-285.
Hu, C. S., Tanaka, C., and Ohtani, T. 2004. Locating and Identifying Sound Knots and Dead Knots on Sugi by the Rulebased Color Vision System. Journal of Wood Science. 50(2): 115-122.
Pham, D., Eldukhri, E., Soroka, A., Ruza’b, G., and Estéveza, P. 2005. Image Segmentation Using Fuzzy Min-Max Neural Networks for Wood Defect Detection. Intelligent Production Machines and Systems Conference. 183.
Ruz, G. A., Estevez, P. A., and Perez, C. A. 2005. A Neurofuzzy Color Image Segmentation Method for Wood Surface Defect Detection. Forest Products Journal. 55(4): 52-58.
Ziadi, A., Ntawiniga, F. F. F. F., and Maldague, X. 2007. Neural Networks for Color Image Segmentation: Application To Sapwood Assessment. Canadian Conference on Electrical and Computer Engineering. 417-420. IEEE.
Kurdthongmee, W. 2008. Colour Classification of Rubberwood Boards for Fingerjoint Manufacturing Using a SOM Neural Network and Image Processing. Computers and Electronics in Agriculture. 64(2): 85-92.
Lee, S., and Araman, P. 2009. Automated Grading, Upgrading, and Cuttings Prediction of Surfaced Dry Hardwood Lumber. Image and Signal Processing and Analysis, 2009. ISPA 2009. Proceedings of 6th International Symposium on. 371-376.
Wang, L., Li, L., Qi, W., and Yang, H. 2009. Pattern Recognition and Size Determination of Internal Wood Defects Based on Wavelet Neural Networks. Computers and Electronics in Agriculture. 69(2): 142-148.
Ni Song, Y., Xu Hua, D., and Wang Li, H. 2011. Quantitative Identification of Defects in Lumber Based on Modal Frequencies And Artificial Neural Network. Advanced Materials Research. 183-185: 2279-2283.
Pervan, S., Brezovic, M., Prekrat, S., Klaric, M., and Sazdevski, G. 2012. Possibilities for Thermography Application in Hydrothermal Wood Processing. Drvna Industrija. 63(4): 277-281.
Lopez, G., Basterra, L. A., Ramon-Cueto, G., and de Diego, A. 2014. Detection of Singularities and Subsurface Defects in Wood by Infrared Thermography. International Journal of Architectural Heritage. 8(4): 517-536.
Thomas, L., and Mili, L. 2007. A Robust GM-Estimator for the Automated Detection of External Defects on Barked Hardwood Logs and Stems. IEEE Transactions on Signal Processing. 55(7): 3568-3576.
Thomas, L., and Mili, L. 2004. Defect Detection on Hardwood Logs Using High Resolution Three-Dimensional Laser Scan Data. International Conference on Image Processing. 243-246. IEEE.
Cho, T. T.-H., Conners, R. W., and Araman, P. A. 1990. A Computer Vision System for Automated Grading of Rough Hardwood Lumber Using a Knowledge-based Approach. IEEE International Conference on Systems, Man, and Cybernetics. 0-5.
Niskanen, M. 2001. Experiments with SOM based Inspection of Wood. International Conference on Quality Control by Artificial Vision (QCAV2001). 311-316.
De Andrade, M. G., and Gonzaga, A. 1996. Feature Extraction for Defect Classification in Surfaces of Wooden Boards. Proceedings II Workshop on Cybernetic Vision. 185-189. IEEE Comput. Soc.
Rodrigues, E. L. L., and Roda, V. O. 2005. Methodology for Classification of Images Based on the Characteristics of a New Interpretation of Shannon’s Entropy. Wood Science and Technology. 39(2): 113-128.
Nagai, H., Murata, K., and Nakano, T. 2009. Defect Detection in Lumber Including Knots Using Bending Deflection Curve: Comparison Between Experimental Analysis and Finite Element Modeling. Journal of Wood Science. 55: 169-174.
Conners, R., Ng, C., and Drayer, T. 1990. Computer vision hardware system for automating rough mills of furniture plants. Applications of Artificial Intelligence VIII. 1293(1990).
Pham, D. T., and Alcock, R. J. 1998. Artificial Intelligence Based Techniques for Processing Segmented Images of Wood Boards. Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering. 212(2): 119-129.
Kauppinen, H., Rautio, H., and Silven, O. 1999. Non-segmenting Defect Detection and SOM Based Classification for Surface Inspection Using Color Vision. Proceedings of SPIE The International Society for Optical Engineering. 3826: 270-280.
Kauppinen, H. 2000. A Two Stage Defect Recognition Method for Parquet Slab Grading. Proceedings 15th International Conference on Pattern Recognition. ICPR-2000. 4(1): 803-806. IEEE Comput. Soc.
Kim, C.-W., and Koivo, A. J. 1990. Hierarchical Classification of Surface Defects on Dusty Wood Boards. [1990] Proceedings. 10th International Conference on Pattern Recognition. i: 775-779. IEEE Comput. Soc. Press.
Ruz, G. A., Estevez, P. A., and Ramirez, P. A. 2009. Automated Visual Inspection System for Wood Defect Classification Using Computational Intelligence Techniques. International Journal of Systems Science. 40(2): 163-172.
Kauppinen, H. 1999. Development of a Color Machine Vision Method for Wood Surface Inspection. University of Oulu.
Pham, D. T., and Alcock, R. J. 1998. Automated Grading and Defect Detection: A Review. Forest Products Journal. 48(4): 34-42.
Athilakshmi, R. 2010. Defect Identification of Lumber Through Correlation Technique with Statistical and Textural Feature Extraction Method. International Conference on Communication and Computational Intelligence. 524-528.
Cavalin, P., Oliveira, L. S., Koerich, A. L., Britto, A. S., Jr, A. S. B., and Jr., A. S. B. 2006. Wood Defect Detection Using Grayscale Images and an Optimized Feature Set. IECON Proceedings (Industrial Electronics Conference). 3408-3412.
Gu, I. Y.-H., Andersson, H., and Vicen, R. 2010. Wood Defect Classification Based on Image Analysis and Support Vector Machines. Wood Science and Technology. 44(4): 693-704.
Zhang, Y., Tong, C., Yu, H., and Cao, J. 2012. Wood Board Defects Sorting Based on Method of Possibilistic C-Means improved Support Vector Data Description. Applied Mechanics and Materials. 128-129: 1288-1291.
Wang, Y., Wang, H., and Mo, L. 2010. Research on Recognition of Wood Texture Based on Integrated Neural Network Classifier. 2010 International Conference on Intelligent Control and Information Processing. 512-515.
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