Abstract
AbstractIn the casting process of the steel plate, due to the influence of rolling equipment and technology, the defects such as cracks and scratches appear on the surface of steel plate, which affect the performance of steel plate and even cause production accidents. In this paper, an automatic detection method for steel plate scratch is proposed. Firstly, the steel plate image is decomposed by channel and the enhanced image is obtained by the improved MSR (Multi-Scale Retinex) enhancement algorithm. Then, the phase consistency is detected after the Log Gabor wavelet transform and the scratch areas are obtained by the threshold segmentation and intersection of three channels. Finally, the scratch position is identified and the scratch characteristics such as width and length can be calculated. The results show that the minimum error of the characteristics measurement is only 2.28% in the experimental environment and 4.15% in the field environment, and the mean running time is 0.2826 s in the experimental environment and 0.3193 s in the field environment. It verifies that the proposed method is effective and practical.
Funder
State key laboratory open project of China National Heavy Machinery Research Institute
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Reference25 articles.
1. Qin, Y.R.: Steel surface quality and development of intelligent detection. Nangang Technol. Manag. 2, 32–33 (2020)
2. Zaini, M.A.H.P., Saari, M.M., Nadzri, N.A., Aziz, Z., Ramlan, N.H., Tsukada, K.: Extraction of flux leakage and eddy current signals induced by submillimeter backside slits on carbon steel plate using a low-field amr differential magnetic probe. IEEE Access 9, 146755–146770 (2021)
3. Wang, G., Xiao, Q., Guo, M., Yang, J.: Optimal frequency of AC magnetic flux leakage testing for detecting defect size and orientation in thick steel plates. IEEE Trans. Magn. 57(9), 1–8 (2021)
4. Zhang,M., Sun, W., Wang, J.: Steel surface defect detection based on adaptive threshold gray transformation. In: 2021 IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS), Shenyang, Liaoning, China, pp. 280–283. (2021)
5. Liu, K., Luo, N., Li, A., Tian, Y., Sajid, H., Chen, H.: A new self-reference image decomposition algorithm for strip steel surface defect detection. IEEE Trans. Instrum. Meas. 69(7), 4732–4741 (2020)
Cited by
3 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献