A Texture Removal Method for Surface Defect Detection in Machining
Bibliographic record
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: Industrial Vision Systems and Defect Detection · Infrastructure Maintenance and Monitoring · Surface Roughness and Optical Measurements
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (39)
Show source-provided references
1. Huang, S.H., Pan, Y.C.: Automated visual inspection in the semiconductor industry: a survey. Comput. Ind. 66, 1–10 (2015). https://doi.org/10.1016/j.compind.2014.10.00610.1016/j.compind.2014.10.006 ↗
2. Peng, L., Liu, J.: Detection and analysis of large-scale WT blade surface cracks based on UAV-taken images. IET Image Process 12, 2059–2064 (2018). https://doi.org/10.1049/iet-ipr.2018.554210.1049/iet-ipr.2018.5542 ↗
3. Reddy, A., Indragandhi, V., Ravi, L., Subramaniyaswamy, V.: Detection of cracks and damage in wind turbine blades using artificial intelligence-based image analytics. Measurement (2019). https://doi.org/10.1016/j.measurement.2019.07.05110.1016/j.measurement.2019.07.051 ↗
4. Wang, Y.N., Yoshihashi, R., Kawakami, R., You, S.D., Harano, T., Ito, M.: Unsupervised anomaly detection with compact deep features for wind turbine blade images taken by a drone. IPSJ Trans. Comput. Vis. Appl. 11, 3–7 (2019). https://doi.org/10.1186/s41074-019-0056-010.1186/s41074-019-0056-0 ↗
5. Aleixos, N., Blasco, J., Navarrón, F., Moltó, E.: Multispectral inspection of citrus in real-time using machine vision and digital signal processors. Comput. Electron. Agric. 33, 121–137 (2002). https://doi.org/10.1016/S0168-1699(02)00002-910.1016/S0168-1699(02)00002-9 ↗
6. Tessier, J., Duchesne, C., Bartolacci, G.: A machine vision approach to on-line estimation of run-of-mine ore composition on conveyor belts. Miner. Eng. 20, 1129–1144 (2007). https://doi.org/10.1016/j.mineng.2007.04.00910.1016/j.mineng.2007.04.009 ↗
7. Jian, C.X., Gao, J., Ao, Y.H.: Automatic surface defect detection for mobile phone screen glass based on machine vision. Appl. Soft Comput. 52, 348–358 (2017). https://doi.org/10.1016/j.asoc.2016.10.03010.1016/j.asoc.2016.10.030 ↗
8. Wang, L., Zhang, Z.J.: Automatic detection of wind turbine blade surface cracks based on UAV-taken images. IEEE Trans. Ind. Electron. 64, 7293–7303 (2017). https://doi.org/10.1109/TIE.2017.268203710.1109/TIE.2017.2682037 ↗
9. Wang, L., Zhang, Z.J., Luo, X.: A two-stage data-driven approach for image-based wind turbine blade crack inspections. IEEE-ASME Trans. Mechatron. 24, 1271–1281 (2019). https://doi.org/10.1109/TMECH.2019.290823310.1109/TMECH.2019.2908233 ↗
10. Qu, Z., Lin, L.D., Guo, Y., Wang, N.: An improved algorithm for image crack detection based on percolation model. IEEJ Trans. Electr. Electron. Eng. 10, 214–221 (2015). https://doi.org/10.1002/tee.2205610.1002/tee.22056 ↗
11. Zhu, Z.H., German, S., Brilakis, I.: Visual retrieval of concrete crack properties for automated post-earthquake structural safety evaluation. Autom. Constr. 20, 874–883 (2011). https://doi.org/10.1016/j.autcon.2011.03.00410.1016/j.autcon.2011.03.004 ↗
12. Yamaguchi, T., Hashimoto, S.: Fast crack detection method for large-size concrete surface images using percolation-based image processing. Mach. Vis. Appl. 21, 797–809 (2010). https://doi.org/10.1007/s00138-009-0189-810.1007/s00138-009-0189-8 ↗
13. Kim, H., Lee, J., Ahn, E., Cho, S., Shin, M., Sim, S.H.: Concrete crack identification using a UAV incorporating hybrid image processing. Sensors (2017). https://doi.org/10.3390/s1709205210.3390/s17092052 ↗
14. Nguyen, H.N., Kam, T.Y., Cheng, P.Y.: Automatic crack detection from 2D images using a crack measure-based B-spline level set model. Multidimens. Syst. Signal Process 29, 213–244 (2018). https://doi.org/10.1007/s11045-016-0461-910.1007/s11045-016-0461-9 ↗
15. Hajizadeh, S., Nunez, A., Tax, D.M.J.: Semi-supervised rail defect detection from imbalanced image data. IFAC Papers Online (2016). https://doi.org/10.1016/j.ifacol.2016.07.01410.1016/j.ifacol.2016.07.014 ↗
16. Elharrouss, O., Moujahid, D., Tairi, H.: Motion detection based on the combining of the background subtraction and the struction-texture decomposition. Optik 126, 5992–5997 (2015). https://doi.org/10.1109/ISACV.2015.710554810.1109/ISACV.2015.7105548 ↗
17. Navarro, P.J., Fernández-Isla, C., Alcover, P.M., Suardiaz, J.: Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level. Sensors (2016). https://doi.org/10.3390/s1608117810.3390/s16081178 ↗
18. Win, M., Bushroa, A.R., Hassan, M.A., Hilman, N.M., Ide-Ektessabi, A.: A contrast adjustment thresholding method for surface defect detection based on mesoscopy. IEEE Trans. Ind. Inform. 11, 642–649 (2015). https://doi.org/10.1109/TII.2015.241767610.1109/TII.2015.2417676 ↗
19. Hoang, D.N.: Detection of surface crack in building structures using image processing technique with an improved otsu method for image thresholding. Adv. Civ. Eng. (2018). https://doi.org/10.1155/2018/392412010.1155/2018/3924120 ↗
20. Fujita, Y., Hamamoto, Y.: A robust automatic crack detection method from noisy concrete surfaces. Mach. Vis. Appl. 22, 245–254 (2011). https://doi.org/10.1007/s00138-009-0244-510.1007/s00138-009-0244-5 ↗
21. Zhang, W.Y., Zhang, Z.J., Qi, D.P., Liu, Y.: Automatic crack detection and classification method for subway tunnel safety monitoring. Sensors 14, 19307–19328 (2014). https://doi.org/10.3390/s14101930710.3390/s141019307 ↗
22. Landstrom, A., Thurley, M.J.: Morphology-based crack detection for steel slabs. IEEE J. Sel. Top. Signal Process. 6, 866–875 (2012). https://doi.org/10.1109/JSTSP.2012.221241610.1109/JSTSP.2012.2212416 ↗
23. Cha, Y.J., Choi, W., Buyukozturk, O.: Deep learning-based crack defect detection using convolutional neural networks. Comput.-Aided Civ. Infrastruct. Eng. 32, 361–378 (2017). https://doi.org/10.1111/mice.1226310.1111/mice.12263 ↗
24. Lin, T.H., Way, D.L., Shih, Z.C., Tai, W.K., Chang, C.C.: An efficient structure-aware bilateral texture filtering for image smoothing. Comput. Graph. Forum 35, 57–66 (2016). https://doi.org/10.1111/cgf.1300310.1111/cgf.13003 ↗
25. Herrero, F.J.D.L.C., Garcia, D.F., Usamentiaga, R.: Inspection system for rail surfaces using differential images. IEEE Trans. Ind. Appl. 54, 4948–4957 (2018). https://doi.org/10.1109/TIA.2018.283260610.1109/TIA.2018.2832606 ↗
26. Shi, Y., Cui, L.M., Qi, Z.Q., Meng, F., Chen, Z.S.: Automatic road crack detection using random structured forests. IEEE Trans. Intell. Transp. Syst. 17, 3434–3445 (2016). https://doi.org/10.1109/TITS.2016.255224810.1109/TITS.2016.2552248 ↗
27. Ai, D.H., Jiang, G.Y., Kei, L.S., Li, C.W.: Automatic pixel-level pavement crack detection using information of multi-scale neighborhoods. IEEE Access 6, 24452–24463 (2018). https://doi.org/10.1109/ACCESS.2018.282934710.1109/ACCESS.2018.2829347 ↗
28. Huang, D.P., Liao, S.P., Sunny, A.I., Yu, S.D.: A novel automatic surface scratch defect detection for fluid-conveying tube of Coriolis mass flow-meter based on 2D-direction filter. Measurement 126, 332–341 (2018). https://doi.org/10.1016/j.measurement.2018.05.06010.1016/j.measurement.2018.05.060 ↗
29. Wang, J.Z., Li, Q.Y., Gan, J.R., Yu, H.M., Yang, X.: Surface defect detection via entity sparsity pursuit with intrinsic priors. IEEE Trans. Ind. Inform. 16, 141–150 (2020). https://doi.org/10.1109/TII.2019.291752210.1109/TII.2019.2917522 ↗
30. Liu, K., Wang, H.Y., Chen, H.Y., Qu, E.Q., Tian, Y., Sun, H.X.: Steel surface defect detection using a new Haar-Weibull-variance model in unsupervised manner. IEEE Trans. Instrum. Meas. 66, 2585–2596 (2017). https://doi.org/10.1109/TIM.2017.27128310.1109/TIM.2017.271283 ↗
31. Mokhtari, S., Wu, L.L., Yun, H.B.: Statistical selection and interpretation of imagery features for computer vision-based pavement crack-detection systems. J. Perform. Constr. Facil. (2017). https://doi.org/10.1061/(ASCE)CF.1943-5509.000100610.1061/(ASCE)CF.1943-5509.0001006 ↗
32. Yun, J.P., Choi, D.C., Jeon, Y.J., Park, C., Kim, S.W.: Defect inspection system for steel wire rods produced by hot rolling process. Int. J. Adv. Manuf. Technol. 70, 1625–1634 (2014). https://doi.org/10.1007/s00170-013-5397-810.1007/s00170-013-5397-8 ↗
33. Amhaz, R., Chambon, S., Idier, J., Baltazart, V.: Automatic crack detection on two-dimensional pavement images: an algorithm based on minimal path selection. IEEE Trans. Intell. Transp. Syst. 17, 2718–2729 (2016). https://doi.org/10.1109/TITS.2015.247767510.1109/TITS.2015.2477675 ↗
34. Yu, X.F., Yan, Y.Q., Zhao, J., Sheng, W., Li, Z.M.Q.: Non-contact inspection method for surface roughness on small samples. Surf. Topogr.-Metrol. (2023). https://doi.org/10.1088/2051-672X/acce5010.1088/2051-672X/acce50 ↗
35. Solis-Perez, J.E., Hernandez, J.A., Parrales, A., Gomez-Aguilar, J.F., Huicochea, A.: Artificial neural networks with conformable transfer function for improving the performance in thermal and environmental processes. Neural Netw. 152, 44–56 (2022). https://doi.org/10.1016/j.neunet.2022.04.01610.1016/j.neunet.2022.04.016 ↗
36. Hui, Y., Wu, J.S., Yu, B.: 3D construction of the human spine cervical segment and statistical analysis of scientific measurement. In: 2016 First IEEE International Conference on Computer Communication and the Internet (ICCCI 2016), pp. 507–513. (2016)10.1109/CCI.2016.7778975 ↗
37. Suh, Y., Kim, H.: Probabilistic class histogram equalization based on posterior mean estimation for robust speech recognition. IEEE Signal Process. Lett. 22, 2421–2424 (2015). https://doi.org/10.1109/LSP.2015.249020210.1109/LSP.2015.2490202 ↗
38. Deng, L.W., Guo, Y.G., Char, B.R.: Defect detection on a wind turbine blade based on digital image processing. Processes 9, 1452 (2021). https://doi.org/10.3390/pr908145210.3390/pr9081452 ↗
39. Liu, G.H., Zheng, X.T.: Fabric defect detection based on information entropy and frequency domain saliency. Vis. Comput.Comput. 37, 515–528 (2020). https://doi.org/10.1007/s00371-020-01820-w10.1007/s00371-020-01820-w ↗