Journal
JOURNAL OF UNIVERSITY OF SCIENCE AND TECHNOLOGY BEIJING
Volume 14, Issue 5, Pages 437-442Publisher
INT JOURNAL MINERALS METALLURGY & MATERIALS, EDITORIAL DEPT
DOI: 10.1016/S1005-8850(07)60086-3
Keywords
cold rolled strip; surface defect; neural networks; fast Fourier transform (FFT); feature extraction and optimization; genetic algorithm; feature set
Ask authors/readers for more resources
Considering that the surface defects of cold rolled strips are hard to be recognized by human eyes under high-speed circumstances, an automatic recognition technique was discussed. Spectrum images of defects can be got by fast Fourier transform (FFT) and sum of valid pixels (SVP), and its optimized center region, which concentrates nearly all energies, are extracted as an original feature set. Using genetic algorithm to optimize the feature set, an optimized feature set with 51 features can be achieved. Using the optimized feature set as an input vector of neural networks, the recognition effects of LVQ neural networks have been studied. Experiment results show that the new method can get a higher classification rate and can settle the automatic recognition problem of surface defects on cold rolled strips ideally.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available