Issue34

Z. Jijun et alii, Frattura ed Integrità Strutturale, 34 (2015) 590-598; DOI: 10.3221/IGF-ESIS.34.65 598 will are often affected by oil contamination and other dirt on the surface, which will be mistakenly identified as defect; some of the damages on the surface of wire rope are not so obvious, therefore the wire will be identified as being in good condition. But the misjudgment rate is overall low, and the misjudgment rate of the wire rope with defect on the surface is lower than that of the wires with good surface. So this can replace the manpower to detect the wire ropes. C ONCLUSION he method of filtering realizes the separation of the texture and background of the wire ropes. The defect detection of wire rope for oil well based on angle adaptive can reach a relatively high accuracy and speed. It’s applicable for the online real-time detection of the wire rope. Identifying the surface damage of wire ropes by the detection method proposed in this article can meet the needs of the actual detection. R EFERENCES [1] Xv, J., The intelligent detection technique study for broken wires of the steel wire rope, Wuhan University of Technology, 2002. [2] Shen, Y., Zhang, D., Ge, S., Effect of fretting amplitudes on fretting wear behavior of steel wires in coal mines, Mining Science and Technology, 20 (2010) 803-808. [3] Zhang, D., Ge, S., Xiong, D., Fretting wear of steel wires in hoisting ropes , International Journal of Minerals Metallurgy and Materials, 9(2) (2002) 81-84. [4] Wang, Y., Long, X., Reduce non-uniform illumination with wavelet, Optical Technique, 5 (2005) 726-728. DOI: 10.13741/j.cnki.11-1879/o4.2005.05.026 [5] Wen, S., You, Z., Performance optimization homomorphic filtering algorithm, Application Research of Computers, 3 (2000) 62-65. [6] Bertalmío, M., Caselles, V., Provenzi, E., Issues about retinex theory and contrast enhancement, International Journal of Computer Vision, 83(1) (2009) 101-119. DOI: 10.1007/s11263-009-0221-5 [7] Ma, Y., Gu, X., Wang, Y., Feature fusion method for edge detection of color images, Journal of Systems Engineering and Electronics, 20(2) (2009) 394-399. [8] Li, J., Huang, P., Wang, X., Pan, X., Image edge detection based on beamlet transform, Journal of Systems Engineering and Electronics, 20(1) (2009) 1-5. [9] Dou, Z., Shi, P., Lin, Y., A kind of edge detection algorithm with edge-preserving characteristics, Journal of Harbin Institute of Technology, 20(2) (2013) 86-89. [10] Wang, B., Using roberts operator for edge processing, Gansu Science and Technology, 10 (2008) 18-20. [11] Ma, Y., Zhang, Z., Comparison of several edge detection operator, Industry and Mine Automation, 1 (2004) 54-56. [12] Ding, L., Goshtasby, A., On the canny edge detector, Pattern Recognition, 34(3) (2001) 721-725. DOI: 10.1016/S0031-3203(00)00023-6. [13] Feng, J., Liu, W., Yu, S., Eyes location based on gray-level integration projection, Computer Simulation, 4 (2005) 75- 76,104. [14] Jan, F., Usman, I., Agha, S., Reliable iris localization using integral projection function and 2D-shape properties, Chinese Optics Letters, 11 (2012) 111501(1)-111501(6). DOI: 10.3788/COL201210.111501 [15] Kim, J., Park, R., A fast feature-based block matching algorithm using integral projections, IEEE Journal on Selected Areas in Communications, 10(5) (1992) 968-971. DOI: 10.1109/49.139002 [16] Mao, L., Kong, F., et al., The wire rope surface defect detection based on image processing and neural network, Measurement & Control Technology, 7 (2007) 23-25 [17] Gao, H., Yang, S., Yang, K., et al., A neural network-based technique for quantitative wire rope inspection, NDT & E International, 26(1) (1993) 31-33. [18] Fan, J., Du, Y., Zhou, Y., Wang, Y., Edge detection of range images using genetic neural networks, Journal of Chongqing University of Posts and Telecommunications (Natural Science Edition), 21(2) (2009) 272-275. [19] Liu, X., Deng, Z., Wang, T., Real estate appraisal system based on GIS and BP neural network, Transactions of Nonferrous Metals Society of China, 21 (2011) 626-630. DOI: 10.1016/S1003-6326(12)61652-5. [20] Zhang, L., Zhao, J., Zhang, X., Zhang, E., Study of a new improved PSO-BP neural network algorithm, Journal of Harbin Institute of Technology, 20(5) (2013) 106-112. T