Improved detection of subsurface defects through active thermography and ensembling techniques

References

• Bai, B., J. Tian, and T. Wang. 2022. YUSEG: Yolo and Unet for cell instance segmentation is all you need.
   Google Scholar
• Bang, H.-T., S. Park, and H. Jeon. 2020. Defect identification in composite materials via thermography and deep learning techniques. Composite Structures 246:112405. https://www.sciencedirect.com/science/article/pii/S026382232030146X. doi:10.1016/j.compstruct.2020.112405.
   Web of Science ®Google Scholar
• Bochkovskiy, A., C.-Y. Wang, and H.-Y M. Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv Preprint arXiv:2004.10934.
   Google Scholar
• Bodla, N., B. Singh, R. Chellappa, and L. S. Davis. 2017. Soft-NMS–improving object detection with one line of code. Proceedings of the IEEE International Conference on Computer Vision, 5561–9.
   Google Scholar
• Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40. doi:10.1007/BF00058655.
   Web of Science ®Google Scholar
• Breiman, L. 2001. Random Forests. Machine Learning 45 (1):5–32. doi:10.1023/A:1010933404324.
   Web of Science ®Google Scholar
• Casado-García, Á., and J. Heras. 2020. Ensemble methods for object detection. In ECAI 2020, 2688–95. IOS Press.
   Google Scholar
• Chen, L.-C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2014. Semantic image segmentation with deep convolutional nets and fully connected CRFs. arXiv Preprint arXiv:1412.7062. doi:10.48550/arxiv.1412.7062.
   Google Scholar
• Chen, L.-C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2018a. DeepLab: Semantic Image Segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (4):834–48. doi:10.1109/TPAMI.2017.2699184.
   PubMed Web of Science ®Google Scholar
• Chen, L.-C., G. Papandreou, F. Schroff, and H. Adam. 2017. Rethinking atrous convolution for semantic image segmentation. arXiv Preprint arXiv:1706.05587. doi:10.48550/arxiv.1706.05587.
   Google Scholar
• Chen, L.-C., Y. Zhu, G. Papandreou, F. Schroff, and H. Adam. 2018b. Encoder-decoder with atrous separable convolution for semantic image segmentation. Proceedings of the European Conference on Computer Vision (ECCV), 801–18.
   Google Scholar
• Chen, T., and C. Guestrin. 2016. XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, New York, NY, USA, 785–794. Association for Computing Machinery. doi:10.1145/2939672.2939785.
   Google Scholar
• Cruz-Vega, I., D. Hernandez-Contreras, H. Peregrina-Barreto, J. d. J. Rangel-Magdaleno, and J. M. Ramirez-Cortes. 2020. Deep learning classification for diabetic foot thermograms. Sensors 20 (6):1762. https://www.mdpi.com/1424-8220/20/6/1762. doi:10.3390/s20061762.
   PubMed Web of Science ®Google Scholar
• Estrada-Pérez, L. V., S. Pradana-López, A. M. Pérez-Calabuig, M. L. Mena, J. C. Cancilla, and J. S. Torrecilla. 2021. Thermal imaging of rice grains and flours to design convolutional systems to ensure quality and safety. Food Control.121:107572. https://www.sciencedirect.com/science/article/pii/S0956713520304886. doi:10.1016/j.foodcont.2020.107572.
   Web of Science ®Google Scholar
• Everingham, M., S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2015. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision 111 (1):98–136. doi:10.1007/s11263-014-0733-5.
   Web of Science ®Google Scholar
• Everingham, M., L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman. 2010. The pascal visual object classes (VOC) challenge. International Journal of Computer Vision 88 (2):303–38. doi:10.1007/s11263-009-0275-4.
   Web of Science ®Google Scholar
• Fang, Q., C. Ibarra-Castanedo, and X. Maldague. 2021. Automatic defects segmentation and identification by deep learning algorithm with pulsed thermography: Synthetic and experimental data. Big Data and Cognitive Computing 5 (1):9. https://www.mdpi.com/2504-2289/5/1/9. doi:10.3390/bdcc5010009.
   Google Scholar
• Freund, Y., and R. E. Schapire. 1997. A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55 (1):119–39. https://www.sciencedirect.com/science/article/pii/S002200009791504X. doi:10.1006/jcss.1997.1504.
   Web of Science ®Google Scholar
• Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 580–7.
   Google Scholar
• He, K., G. Gkioxari, P. Dollar, and R. Girshick. 2017. Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), October.
   Google Scholar
• Hossin, M., and M. N. Sulaiman. 2015. A review on evaluation metrics for data classification evaluations. International Journal of Data Mining & Knowledge Management Process 5 (2):01–11. doi:10.5121/ijdkp.2015.5201.
   Google Scholar
• Inagaki, T., T. Ishii, and T. Iwamoto. 1999. On the NDT and E for the diagnosis of defects using infrared thermography. NDT & E International 32 (5):247–57. https://www.sciencedirect.com/science/article/pii/S0963869598000590. doi:10.1016/S0963-8695(98)00059-0.
   Web of Science ®Google Scholar
• Izquierdo, M., M. Lastra-Mejías, E. González-Flores, J. C. Cancilla, M. Pérez, and J. S. Torrecilla. 2020. Convolutional decoding of thermographic images to locate and quantify honey adulterations. Talanta 209:120500. https://www.sciencedirect.com/science/article/pii/S0039914019311336. doi:10.1016/j.talanta.2019.120500.
   PubMed Web of Science ®Google Scholar
• Ji, R., and L. Qi. 2011. Crop-row detection algorithm based on Random Hough Transformation. Mathematical and Computer Modelling 54 (3–4):1016–20. https://www.sciencedirect.com/science/article/pii/S0895717710005212. doi:10.1016/j.mcm.2010.11.030.
   Google Scholar
• Jocher, G., A. Stoken, A. Chaurasia, J. Borovec, T. Xie, Y. Kwon, NanoCode012, et al. 2021. ultralytics/yolov5: V 6.0 - YOLOv5n ‘Nano’ models, Roboflow integration, TensorFlow export, OpenCV DNN support. October. doi:10.5281/zenodo.5563715.
   Google Scholar
• Lema, D. G., O. D. Pedrayes, R. Usamentiaga, P. Venegas, and D. F. García. 2022. Automated detection of subsurface defects using active thermography and deep learning object detectors. IEEE Transactions on Instrumentation and Measurement 71:1–13. doi:10.1109/TIM.2022.3169484.
   Web of Science ®Google Scholar
• Lin, T.-Y., M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. 2014. Microsoft COCO: Common objects in context. In Computer vision – ECCV 2014, edited by David Fleet, Tomas Pajdla, Bernt Schiele, and Tinne Tuytelaars, 740–55. Cham: Springer International Publishing.
   Google Scholar
• Liu, K., Q. Yu, Y. Liu, J. Yang, and Y. Yao. 2022. Convolutional graph thermography for subsurface defect detection in polymer composites. IEEE Transactions on Instrumentation and Measurement 71:1–11. doi:10.1109/TIM.2022.3205906.
   Web of Science ®Google Scholar
• Liu, W., D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg. 2016. SSD: Single shot MultiBox detector. In Computer vision – ECCV 2016, edited by Bastian Leibe, Jiri Matas, Nicu Sebe, and Max Welling, 21–37. Cham: Springer International Publishing.
   Google Scholar
• Lyra, S., L. Mayer, L. Ou, D. Chen, P. Timms, A. Tay, P. Y. Chan, B. Ganse, S. Leonhardt, and C. Hoog Antink. 2021. A deep learning-based camera approach for vital sign monitoring using thermography images for ICU patients. Sensors 21 (4):1495. https://www.mdpi.com/1424-8220/21/4/1495. doi:10.3390/s21041495.
   PubMed Web of Science ®Google Scholar
• Padilla, R., W. L. Passos, T. L. B. Dias, S. L. Netto, and E. A. B. da Silva. 2021. A comparative analysis of object detection metrics with a companion open-source toolkit. Electronics 10 (3):279. https://www.mdpi.com/2079-9292/10/3/279. doi:10.3390/electronics10030279.
   Web of Science ®Google Scholar
• Pedrayes, O. D., D. G. Lema, R. Usamentiaga, P. Venegas, and D. F. García. 2022. Semantic segmentation for non-destructive testing with step-heating thermography for composite laminates. Measurement 200:111653. https://www.sciencedirect.com/science/article/pii/S0263224122008624. doi:10.1016/j.measurement.2022.111653.
   Web of Science ®Google Scholar
• Redmon, J., S. Divvala, R. Girshick, and A. Farhadi. 2016. You only look once: Unified, real-time object detection. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June, 779–88.
   Google Scholar
• Redmon, J., and A. Farhadi. 2017. YOLO9000: Better, faster, stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July, 7263–71.
   Google Scholar
• Redmon, J., and A. Farhadi. 2018. YOLOv3: An incremental improvement. arXiv Preprint arXiv:1804.02767. doi:10.48550/arxiv.1804.02767.
   Google Scholar
• Ren, S., K. He, R. Girshick, and J. Sun. 2017. Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (6):1137–49. doi:10.1109/TPAMI.2016.2577031.
   PubMed Web of Science ®Google Scholar
• Reza, A. M. 2004. Realization of the contrast limited adaptive histogram equalization (CLAHE) for real-time image enhancement. Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology 38 (1):35–44. doi:10.1023/B:VLSI.0000028532.53893.82.
   Google Scholar
• Rong, W., Z. Li, W. Zhang, and L. Sun. 2014. An improved Canny edge detection algorithm. 2014 IEEE International Conference on Mechatronics and Automation, 577–82.
   Google Scholar
• Ronneberger, O., P. Fischer, and T. Brox. 2015. U-Net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention – MICCAI 2015, edited by Nassir Navab, Joachim Hornegger, William M. Wells, and Alejandro F. Frangi, 234–41. Cham: Springer International Publishing.
   Google Scholar
• Solovyev, R., W. Wang, and T. Gabruseva. 2021. Weighted boxes fusion: Ensembling boxes from different object detection models. Image and Vision Computing 107:104117. https://www.sciencedirect.com/science/article/pii/S0262885621000226. doi:10.1016/j.imavis.2021.104117.
   Web of Science ®Google Scholar
• Usamentiaga, R., P. Venegas, J. Guerediaga, L. Vega, and I. López. 2013. A quantitative comparison of stimulation and post-processing thermographic inspection methods applied to aeronautical carbon fibre reinforced polymer. Quantitative InfraRed Thermography Journal 10 (1):55–73. doi:10.1080/17686733.2013.774623.
   Web of Science ®Google Scholar
• Usamentiaga, R., C. Ibarra-Castanedo, M. Klein, X. Maldague, J. Peeters, and A. Sanchez-Beato. 2017. Nondestructive evaluation of carbon fiber bicycle frames using infrared thermography. Sensors 17 (11):2679. https://www.mdpi.com/1424-8220/17/11/2679. doi:10.3390/s17112679.
   PubMed Web of Science ®Google Scholar
• Yousefi, B., D. Kalhor, R. Usamentiaga Fernández, L. Lei, C. I. Castanedo, X. P. Maldague, et al. 2018. Application of deep learning in infrared non-destructive testing. QIRT 2018 Proceedings.
   Google Scholar