Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination |
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| Affiliation: | 1. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, Beijing 100083, China;2. National Engineering Research Center of Advanced Rolling Technology, University of Science and Technology Beijing, Beijing 100083, China;1. National Engineering Research Center for Advanced Rolling Technology, University of Science and Technology Beijing, China;2. Collaborative Innovation Center of Steel Technology, University of Science and Technology Beijing, China |
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| Abstract: | In order to precisely extract the image shape feature for the defect detection and classification, the strip steel image needs to firstly be binarized effectively. In this paper, the intelligent information processing, including mathematical morphology and genetic algorithm, is introduced to the strip steel defect image binarization. In order to eliminate the effect of non-uniform illumination and enhance the detailed information of the strip steel defect image, an enhancement operator based on mathematical morphology (EOBMM) is proposed firstly. And then, the binarization method based on genetic algorithm (BMBGA) is applied to the binarization of the strip steel defect image processed by EOBMM. The experiment results show that our method is effective and efficiency in the strip steel defect image binarization and outperforms the traditional image binarization methods, Otsu and Bernsen. |
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| Keywords: | Strip steel defect image Mathematical morphology Genetic algorithm Image binarization Non-uniform illumination EOBMM Top-hat transformation Fitness function Genetic operations |
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