پیوندهای مفید
آمار
| تعداد نشریات | 21 |
| تعداد شمارهها | 701 |
| تعداد مقالات | 10,097 |
| تعداد مشاهده مقاله | 71,311,274 |
| تعداد دریافت فایل اصل مقاله | 63,034,195 |
Automatic defects detection using neighborhood windows features in tire X-ray images | ||
| International Journal of Nonlinear Analysis and Applications | ||
| دوره 12، Special Issue، اسفند 2021، صفحه 2493-2508 اصل مقاله (1.38 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.6359 | ||
| نویسندگان | ||
| Yousef Sedaghat1؛ Naser Parhizgar* 1؛ Ahmad Keshavarz2 | ||
| 1Department of Electrical Engineering, Shiraz Branch, Islamic Azad University, Shiraz, Iran | ||
| 2IoT and Signal Processing Research Group, Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Bushehr, Iran | ||
| تاریخ دریافت: 20 مرداد 1400، تاریخ بازنگری: 08 آذر 1400، تاریخ پذیرش: 19 آذر 1400 | ||
| چکیده | ||
| Ensuring the production of non-defect high-quality tires is an essential part of the tire industry. X-ray inspection is one of the best methods to detect tire defects. In this paper, a new approach has been presented for detecting tire defects in X-ray images based on an entropy filter, the extraction of texture properties of patches by Local Binary Pattern, and, finally, the classification of defects using the Support Vector Machine method. In the proposed method, an entropy filter was first applied to the input. The parts of the image with different patterns were then selected as candidate regions and these regions were classified by the patch classifier. All the defects were detected and classified and, finally, the efficiency of the algorithm was evaluated. By applying this algorithm to the dataset the best performance was obtained by the LBP descriptor and the linear SVM classifier with 98\% defect location accuracy and 97\% defect detection accuracy were achieved. In order to analyze the performance, used the deep model as a classifier, thus demonstrating that the deep model has a high capability for learning complex patterns. This proposed method is sensitive to local texture and could well describe texture information, which is appropriate for most kinds of tire defects. | ||
| کلیدواژهها | ||
| Tire Defects Detection؛ Local Binary Pattern؛ Entropy Filter؛ Patch Classification؛ Support Vector Machine | ||
| مراجع | ||
|
[1] X. Bai, Y. Fang, W. Lin, L. Wang and B.F. Ju, Saliency-based defect detection in industrial images by using phase spectrum, IEEE Trans. Ind. Inf. 10(4) (2014) 2135–2145. [2] L. Bissi, G. Baruffa, P. Placidi, E. Ricci, A. Scorzoni and P. Valigi, Automated defect detection in uniform and structured fabrics using Gabor filters and PCA, J. Visual Commun. Image Represent. 24(7) (2013) 838–845. [3] X. Chang, C. Gu, J. Liang and X. Xu, Fabric defect detection based on pattern template correction, Math. Prob. Engin. 2018 (2018). [4] C.H. Chien, Y.D. Wu, Y.C. Chen, C.C. Hsieh, T. Chen, Y.T. Chiou, M.L. Tsai, C.T. Wang, S.L. Lin and W.F. Chen, Quantitative detection of internal defects in automotive tires by an interferographic technique, Res. Nondest. Eva. 18(3) (2007) 163–177. [5] X. Cui, Y. Liu, Y. Zhang and C. Wang, Tire defects classification with multi-contrast convolutional neural networks, Int. J. Pattern Recogn. Artificial Intell. 32(4) (2018) 1850011.[6] U. Farooq, T. King, P.H. Gaskell and N. Kapur, Machine vision using image data feedback for fault detection in complex deformable webs, Trans. Institute Measur. Control 26(2) (2004) 119–137. [7] X.F. Feng, X-Ray Tire Defects Automatic Detection System, Dissertation, University of Tianjin, 2008. [8] H. Feng, Z. Jiang, F. Xie, P. Yang, J. Shi and L. Chen, Automatic fastener classification and defect detection in vision-based railway inspection systems, IEEE Trans. Instrument. Measur. 63(4) (2013) 877–888. [9] Q. Guo, C. Zhang, H. Liu and X. Zhang, Defect detection in tire X-ray images using weighted texture dissimilarity, J. Sensors 2016 (2016). [10] Y. Han and P. Shi, An adaptive level-selecting wavelet transform for texture defect detection, Image Vision Comput. 25(8) (2007) 1239–1248. [11] Y.Y. Hung and R.M. Grant, Shearography: A new optical method for nondestructive evaluation of tires, Rubber Chem. Technol. 54(5) (1981) 1042–1050. [12] A. Kumar, Computer-vision-based fabric defect detection: A survey, IEEE Transactions on Industrial Electronics, 55(1) (2008) 348–363. [13] C.F. Kuo and T.L. Su, Gray relational analysis for recognizing fabric defects, Textile Res. J. 73(5) (2003) 461–465. [14] V. Lashkia, Defect detection in X-ray images using fuzzy reasoning, Image Vision Comput. 19(5) (2001) 261–269. [15] Y. Lee and J. Lee, Accurate automatic defect detection method using quadtree decomposition on SEM images, IEEE Trans. Semiconductor Manufact. 27(2) (2014) 223–231. [16] F.Y. Li, The study of an improved fuzzy edge detection algorithm in radial tire quality detection, Adv. Mater. Rese. Trans Tech. Publications Ltd, 317 (2011) 968–971. [17] X. Li, S.K. Tso, X.P. Guan and Q. Huang, Improving automatic detection of defects in castings by applying wavelet technique, IEEE Trans. Ind. Electron. 53(6) (2006) 1927–1934. [18] H. Liu, W. Zhou, Q. Kuang, L. Cao and B. Gao, Defect detection of IC wafer based on spectral subtraction, IEEE Trans. Semiconductor Manufact. 23(1) (2010) 141–147. [19] K.L. Mak and P. Peng, An automated inspection system for textile fabrics based on Gabor filters, Robotics Comput.-Integ. Manufact. 24(3) (2008)359–369. [20] D. Mery and C. Arteta, Automatic defect recognition in x-ray testing using computer vision, IEEE Winter Conf. Appl. Comput. Vision (WACV) IEEE, 2017, pp. 1026–1035. [21] H.Y. Ngan and G.K. Pang, Regularity analysis for patterned texture inspection, IEEE Trans. Automation Sci. Engin. 6(1) (2008) 131–144. [22] H.Y. Ngan, G.K. Pang and N.H. Yung, Performance evaluation for motif-based patterned texture defect detection, IEEE Trans. Automation Sci. Engin. 7(1) (2008) 58–72. [23] H.Y. Ngan, G.K. Pang and N.H. Yung, Automated fabric defect detection–A review, Image Vision Comput. 29(7) (2011) 442–458. [24] J.H. Oh, W.S. Kim, C.H. Han and K.H. Park, Defect detection of TFT-LCD image using adapted contrast sensitivity function and wavelet transform, IEICE Trans. Electron. 90(11) (2007) 2131–2135. [25] T. Ojala, M. Pietikainen and T. Maenpaa, Multiresolution gray-scale and rotation invariant texture classification with local binary patterns, IEEE Trans. Pattern Anal. Machine Intell. 24(7) (2002) 971–987. [26] P.A. Prabha, M. Bharathwaj, K. Dinesh and G.H. Prashath, Defect detection of industrial products using image segmentation and saliency, J. Phys. 1916(1) (2021) 012165. [27] Z. Ren, F. Fang, N. Yan and Y. Wu, State of the art in defect detection based on machine vision, Int. J. Precision Engin. Manufacturing-Green Technol. 2021 (2021) 1–31. [28] Y. Sun, P. Bai, H.Y. Sun and P. Zhou, Real-time automatic detection of weld defects in steel pipe, Ndt & E Int. 38(7) (2005) 522–528. [29] F. Tajeripour, E. Kabir and A. Sheikhi, Fabric defect detection using modified local binary patterns, EURASIP J. Adv. Signal Process. 2008 (2007) 1–12. [30] D.M. Tsai and C.H. Chiang, Automatic band selection for wavelet reconstruction in the application of defect detection, Image Vision Comput. 21(5) (2003) 413–431. [31] D.M. Tsai and T.Y. Huang, Automated surface inspection for statistical textures, Image Vision Comput. 21(4) (2003) 307–323. [32] G. Wang and T.W. Liao, Automatic identification of different types of welding defects in radiographic images, Ndt & E Int. 35(8) (2002) 519–528. [33] M. Win, A.R. Bushroa, M.A. Hassan, N.M. Hilman and A. Ide-Ektessabi, A contrast adjustment thresholding method for surface defect detection based on mesoscopy, IEEE Trans. Indust. Inf. 11(3) (2015) 642–649. [34] Z. Wu, J. Lin and W. Liu, Joint inspection in X-ray♯ 0 belt tire based on periodic texture, Multimedia Tools Appl. 78(7) (2019) 9299–9310. [35] Y. Xiang, C. Zhang and Q. Guo, A dictionary-based method for tire defect detection, IEEE Int. Conf. Inf.Automation (ICIA) IEEE, 2014, pp. 519–523. [36] L. Xu and Q. Huang, Modeling the interactions among neighboring nanostructures for local feature characterization and defect detection, IEEE Trans. Automation Sci. Engin. 9(4) (2012) 745–754. [37] L. Xu and Q. Huang, EM estimation of nanostructure interactions with incomplete feature measurement and its tailored space filling designs, IEEE Trans. Automation Sci. Engin. 10(3) (2013) 579–587. [38] Z. Yan, L. Tao and L. Qing-Ling, Detection of foreign bodies and bubble defects in tire radiography images based on total variation and edge detection, Chinese Phys. Lett. 30(8) (2013) 084205. [39] Y. Zhang, D. Lefebvre and Q. Li, Automatic detection of defects in tire radiographic images, IEEE Trans. Automation Sci. Engin. 14(3) (2015) 1378–1386. [40] Y. Zhang, T. Li and Q. Li, Defect detection for tire laser shearography image using curvelet transform based edge detector, Optics Laser Technol. 47 (2013) 64–71. [41] Y. Zhu, W.Y. Liu, F.C. Liu and J.J. Wang, Inspection of air bubble defect in tires by digital holography, Opt. Prec. Eng. 17 (2009) 1099–1104. | ||
|
آمار تعداد مشاهده مقاله: 44,684 تعداد دریافت فایل اصل مقاله: 50,471 |
||