journal article·2024-12

Artificial Intelligence-Driven Timber Wood Defect Characterization from Terahertz Images

S. Vijayalakshmi, S. Mrudhula, V. Ashok Kumar, Agastin, Varun, A. Mercy Latha

Publisher page

Abstract

No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.

Source: Not provided · Quality: missing

Research context

OpenAlex topics: Remote Sensing and LiDAR Applications · Wood and Agarwood Research · Spectroscopy and Chemometric Analyses

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

References (28)

Show source-provided references
1. Kurdthongmee, W., Suwannarat, K.: Locating wood pith in a wood stem cross sectional image using YOLO object detection. In: Proc. 2019 Int. Conf. on Technologies and Applications of Artificial Intelligence (TAAI), Kaohsiung, Taiwan, pp. 1–6 (2019). https://doi.org/10.1109/TAAI48200.2019.895982310.1109/TAAI48200.2019.8959823 ↗
2. Yi, L.P., Akbar, M.F., Wahab, M.N.A., Rosdi, B.A., Fauthan, M.A., Shrifan, N.H.M.M.: The Prospect of Artificial Intelligence-based Wood Surface Inspection: A review. IEEE Access. 12, 84706–84725 (2024). https://doi.org/10.1109/ACCESS.2024.341292810.1109/ACCESS.2024.3412928 ↗
3. Krügener, K., Sommer, S., Stübling, E., Jachim, R., Koch, M., Viöl, W.: THz properties of typical woods important for european forestry. J. Infrared, Millimeter, and Terahertz Waves. 40(7), 770–774 (2019). https://doi.org/10.1007/s10762-019-00601-410.1007/s10762-019-00601-4 ↗
4. Kashima, M., Tsuchikawa, S., Inagaki, T.: Simultaneous detection of density, moisture content and fiber direction of wood by THz time-domain spectroscopy. J. Wood Sci. 66(1) (2020). https://doi.org/10.1186/s10086-020-01874-310.1186/s10086-020-01874-3 ↗
5. Kashima, M., Tsuchikawa, S., Inagaki, T.: Quality evaluation of engineered wood by THz-TDS. In: Proc. IEEE Int. Radar Symposium (IRS) (2018). https://doi.org/10.1109/irmmw-thz.2018.851015510.1109/irmmw-thz.2018.8510155 ↗
6. Latha, A.M., Rayapureddy, S., Devi, N., Rajagopal, P., Balasubramaniam, K., Pesala, B.: Rapid nondestructive evaluation of defects in GFRP composites using terahertz line scanner. In: Proc. SPIE 11279, Terahertz, RF, Millimeter, and Submillimeter-Wave Technology and Applications XIII (2020). https://doi.org/10.1117/12.254592210.1117/12.2545922 ↗
7. Latha, A.M., Devi, N., Kaimal, H., Pesala, B.: Rapid nondestructive evaluation of fireproof coating degradation using terahertz line scanner. Lecture Notes in Mechanical Engineering, pp. 107–116 (2021). https://doi.org/10.1007/978-981-16-0186-6_1110.1007/978-981-16-0186-6_11 ↗
8. Jagdale, A., Devi, N., Latha, A.M.: Advanced image processing for rapid threat object identification in terahertz images. In: Proc. IEEE Int. Conf. Unmanned Aircraft Systems (ICUAS) (2021). https://doi.org/10.1109/ucmmt53364.2021.956994310.1109/ucmmt53364.2021.9569943 ↗
9. Zhang, J., Xing, W., Xing, M., Sun, G.: Terahertz image detection with the improved faster region-based convolutional neural network. Sensors. 18(7), 2327 (2018). https://doi.org/10.3390/s1807232710.3390/s18072327 ↗
10. Cheng, R., Lucyszyn, S.: Few-shot concealed object detection in Sub-THz security images using improved pseudo-annotations. Sci. Rep. 14(1) (2024). https://doi.org/10.1038/s41598-024-53045-910.1038/s41598-024-53045-9 ↗
11. Cheng, L., Ji, Y., Li, C., Liu, X., Fang, G.: Improved SSD network for fast concealed object detection and recognition in passive terahertz security images. Sci. Rep. 12(1), 12082 (2022). https://doi.org/10.1038/s41598-022-16208-010.1038/s41598-022-16208-0 ↗
12. Lin, T., Wang, R., Shi, Y., Jiang, Z., Yi, S., Wu, Y.: Research on small sample defect detection method based on AnoGAN and U-Net. In: Proc. 8th Int. Conf. on Intelligent Informatics and Biomedical Sciences (ICIIBMS), Okinawa, Japan, pp. 23–25 (2023). https://doi.org/10.1109/ICIIBMS60103.2023.1034788310.1109/ICIIBMS60103.2023.10347883 ↗
13. Wang, A., Togo, R., Ogawa, T., Haseyama, M.: Defect detection of subway tunnels using advanced U-net network. Sensors. 22(6), 2330 (2022). https://doi.org/10.3390/s2206233010.3390/s22062330 ↗
14. Guan, S., Lei, M., Lu, H.: A steel surface defect recognition algorithm based on improved deep learning network model using feature visualization and quality evaluation. IEEE Access. 8, 49885–49895 (2020). https://doi.org/10.1109/ACCESS.2020.297975510.1109/ACCESS.2020.2979755 ↗
15. Aich, S., William, W., Stavness, I.: Jun., Semantic binary segmentation using convolutional networks without decoders, arXiv preprint, (2018). https://doi.org/10.1109/cvprw.2018.0003210.1109/cvprw.2018.00032 ↗
16. Luo, W., Sun, L., An improved binarization algorithm of wood image defect segmentation based on non-uniform background. J. Forestry Res. 30(4), 1527–1533 (2019). https://doi.org/10.1007/s11676-019-00925-w10.1007/s11676-019-00925-w ↗
17. Latha, A.M., Devi, N.: Automatic threat object detection from THz images using artificial intelligence algorithms. In: Proc. Int. Conf. Innovative Electronics Communication (IVEC) (Apr 2023). https://doi.org/10.1109/ivec56627.2023.1015690610.1109/ivec56627.2023.10156906 ↗
18. Zhang, Q., Liu, L., Yang, Z., Yi, J., Jing, Z.: WLSD-YOLO: A model for detecting surface defects in wood lumber. IEEE Access. 12, 1–1 (2024). https://doi.org/10.1109/access.2024.339562310.1109/access.2024.3395623 ↗
19. Zheng, Y., Wang, M., Zhang, B., Shi, X., Chang, Q.: A high-precision and real-time lightweight model for wood defect detection. IEEE Access. 12, 12853–12868 (2024). https://doi.org/10.1109/access.2024.335604810.1109/access.2024.3356048 ↗
20. Wang, R., Liang, F., Wang, B., Mou, X.: ODCA-YOLO: An omni-dynamic convolution coordinate attention-based YOLO for wood defect detection. Forests. 14(9), 1885–1885 (2023). https://doi.org/10.3390/f1409188510.3390/f14091885 ↗
21. Meng, W., Yuan, Y.: SGN-YOLO: detecting wood defects with improved YOLOv5 based on semi-global network. Sensors. 23(21), 8705–8705 (2023). https://doi.org/10.3390/s2321870510.3390/s23218705 ↗
22. Wang, R., Chen, Y., Liang, F., Wang, B., Mou, X., Zhang, G.: BPN-YOLO: A novel method for wood defect detection based on YOLOv7. Forests. 15(7), 1096–1096 (2024). https://doi.org/10.3390/f1507109610.3390/f15071096 ↗
23. Islam, M.M.M., McAteer, C., Prasad, G.: Efficient wafer defect patterns recognition using deep convolutional neural network. In: Proc. 2023 IEEE Conf. on Artificial Intelligence (CAI), Santa Clara, CA, USA, pp. 220–221 (2023). https://doi.org/10.1109/CAI54212.2023.0010210.1109/CAI54212.2023.00102 ↗
24. Nayak, J.P.R., Parameshachari, B.D.: Defect detection in printed circuit boards using leaky-LeNet 5. IGI Global. 10(1), ISSN 2166–7160 (2022). https://doi.org/10.4018/IJSI.30972610.4018/IJSI.309726 ↗
25. UmaRani, V., Srimathi, S., Automatic fabric defect detection using deep CNN-AlexNet models.In: 2nd Int. Conf. on Device Intelligence, Computing and Communication Technologies (DICCT), Dehradun, India, 2024, pp. 1–6 (2024). https://doi.org/10.1109/DICCT61038.2024.1053299110.1109/DICCT61038.2024.10532991 ↗
26. Thalagala, S., Walgampaya, C.: Application of AlexNet convolutional neural network architecture-based transfer learning for automated recognition of casting surface defects. In: Proc. Research Conf. on Smart Computing and Systems Engineering (SCSE), Colombo, Sri Lanka, 2021, pp. 129–136 (2021). https://doi.org/10.1109/SCSE53661.2021.956831510.1109/SCSE53661.2021.9568315 ↗
27. Zhang, J.-Y., Yang, X., Ren, J.-J., Li, L.-J., Zhang, D.-D., Gu, J., Xiong, W.: Terahertz recognition of composite material interfaces based on ResNet-BiLSTM. Measurement. 233, 114771, ISSN 0263–2241 (2024). https://doi.org/10.1016/j.measurement.2024.11477110.1016/j.measurement.2024.114771 ↗
28. Fu, H., Zhou, Z., Zeng, Z., Sang, T., Zhu, Y., Zheng, X., Surface defect detection based on ResNet classification network with GAN optimized. In: Communications: IEEE Smartworld, Ubiquitous Intelligence & Computing, Scalable Computing & Communications, Digital Twin, Privacy Computing, Metaverse, Autonomous & Trusted Vehicles (SmartWorld/UIC//ScalCom/DigitalTwin/PriComp/Meta), Haikou, China, 2022, pp. 1568–1575 (2022). https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.0022510.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00225 ↗