Abstract
Wood, which belongs to organic-based building materials, is useful and natural. Despite the many benefits, environmentally exposed wooden building elements are prone to weathering and gradual damage that significantly reduces the structural durability of aged wooden buildings. To effectively assess the structural health of wooden buildings, it is vital to detect, categorize and localize the damaged wooden elements. This study initially identifies and categorizes the damaged wooden elements, adopting deep convolutional neural network (DCNN) models, named Resnet-50, VGG-16, VGG-19, Inception-V3, and Xception. Afterward, the detected damaged parts are localized using Grad-CAM, Grad-CAM++, and Score-CAM visualization techniques. The obtained results are further improved in terms of classification accuracy and computational cost using the K-mean clustering algorithm. Resnet-50 and Xception models performed best amongst the studied DCNN models, resulting in over 90% classification accuracy. Grad-CAM++ and Score-CAM proved to be better for localization of damaged areas. Besides, compressing the image color with K-mean increases the prediction accuracy by 1% while decreasing the computational cost by more than 60 s.
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References
Ahmad A, Dey L (2007) A K-mean clustering algorithm for mixed numeric and categorical data. Data Knowl Eng 63:503–527. https://doi.org/10.1016/j.datak.2007.03.016
Cavalin P, Oliveira LS, Koerich AL, Britto AS (2006) Wood defect detection using grayscale images and an optimized feature set. IECON Proc (ind Electron Conf). https://doi.org/10.1109/IECON.2006.347618
Chattopadhay A, Sarkar A, Howlader P, Balasubramanian VN (2018) Grad-CAM++: generalized gradient-based visual explanations for deep convolutional networks. In: Proc—2018 IEEE Winter Conf Appl Comput Vision, WACV 2018 2018-Janua, pp 839–847. https://doi.org/10.1109/WACV.2018.00097
Chiang MMT, Mirkin B (2007) Experiments for the number of clusters in K-means. Lecture notes in computer science (including subseries lecture notes in artificial intelligence and lecture notes in bioinformatics). Springer, Berlin, pp 395–405
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proc—30th IEEE conf comput vis pattern recognition, CVPR 2017 2017-Janua, pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195
Han H, Wang WY, Mao BH (2005) Borderline-SMOTE: a new over-sampling method in imbalanced data sets learning. Lecture notes in computer science. Springer, Berlin, pp 878–887
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 770–778
He T, Liu Y, Xu C et al (2019) A fully convolutional neural network for wood defect location and identification. IEEE Access 7:123453–123462. https://doi.org/10.1109/ACCESS.2019.2937461
He T, Liu Y, Yu Y et al (2020) Application of deep convolutional neural network on feature extraction and detection of wood defects. Meas J Int Meas Confed 152:107357. https://doi.org/10.1016/j.measurement.2019.107357
Hu J, Song W, Zhang W et al (2019) Deep learning for use in lumber classification tasks. Wood Sci Technol 53:505–517. https://doi.org/10.1007/s00226-019-01086-z
Jabo S (2011) Machine vision for wood defect detection and classification. Master of Science Thesis, Department of Signals and Systems. Chalmers University of Technology, Göteborg, Sweden
LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 51:436–444
Li Z, Tao D, Li M et al (2019) Prediction of damage accumulation effect of wood structural members under long-term service: a machine learning approach. Materials (basel). https://doi.org/10.3390/ma12081243
Mohebby B, Saei AM (2015) Effects of geographical directions and climatological parameters on natural weathering of fir wood. Constr Build Mater 94:684–690. https://doi.org/10.1016/j.conbuildmat.2015.07.049
Orkin (2020) Termite structural damage—heavy damage from termite infestation. https://www.orkin.com/termites/damage. Accessed 1 July 2020
Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22:1345–1359. https://doi.org/10.1109/TKDE.2009.191
Selvaraju RR, Das A, Vedantam R et al (2016) Grad-CAM: why did you say that?. arxiv:1611.07450
Selvaraju RR, Cogswell M, Das A et al (2020) Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput vis 128:336–359. https://doi.org/10.1007/s11263-019-01228-7
Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. In: 3rd international conference on learning representations, ICLR 2015—conference track proceedings, pp 1–14
Sioma A (2015) Assessment of wood surface defects based on 3D image analysis. Wood Res 60:339–350
Szegedy C, Vanhoucke V, Ioffe S et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, pp 2818–2826
Tomak ED, Ustaomer D, Ermeydan MA, Yildiz S (2018) An investigation of surface properties of thermally modified wood during natural weathering for 48 months. Meas J Int Meas Confed 127:187–197. https://doi.org/10.1016/j.measurement.2018.05.102
Urbonas A, Raudonis V, Maskeliunas R, Damaševičius R (2019) Automated identification of wood veneer surface defects using faster region-based convolutional neural network with data augmentation and transfer learning. Appl Sci 9:4898. https://doi.org/10.3390/app9224898
Wang H, Wang Z, Du M et al (2020) Score-CAM: score-weighted visual explanations for convolutional neural networks. IEEE Comput Soc Conf Comput vis Pattern Recognit Work 2020:111–119. https://doi.org/10.1109/CVPRW50498.2020.00020
Wenshu L, Lijun S, Jinzhuo W (2015) Study on wood board defect detection based on artificial neural network. Open Autom Control Syst J 7:290–295. https://doi.org/10.2174/1874444301507010290
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Hacıefendioğlu, K., Ayas, S., Başağa, H.B. et al. Wood construction damage detection and localization using deep convolutional neural network with transfer learning. Eur. J. Wood Prod. 80, 791–804 (2022). https://doi.org/10.1007/s00107-022-01815-5
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DOI: https://doi.org/10.1007/s00107-022-01815-5