Strip steel target detection algorithm based on single image rapid defog and morphological interframe difference

Miao Peng; Peifeng Hao; Zijun Peng; Caihua Yang

doi:10.1117/12.2557250

3 January 2020 Strip steel target detection algorithm based on single image rapid defog and morphological interframe difference

Miao Peng, Peifeng Hao, Zijun Peng, Caihua Yang

Author Affiliations +

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113732E (2020) https://doi.org/10.1117/12.2557250
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

In the production process of strip steel, detecting wave edge in real time is quite important, otherwise it will contribute to the abandonment of strip steel materials. At this stage, an automatic identification system based on machine vision which aims to figure out the wave edge of strip steel is gradually being put into use. However, it is shown that the complicated environment of the factory makes it difficult to reach its goals. In order to solve this problem, this paper designs a motion strip steel target detection algorithm based on single image rapid defog and morphological interframe difference. Firstly, based on the physical model of foggy image degradation, using a simple mean filtering to estimate the environmental light and global atmospheric light, thus the removal of water mist in video screen is realized. Then, using interframe differential method to extract the motion strip steel in the image, and the noise is filtered by mask segmentation and morphological operator. At last, the experimental results show that the optimization algorithm is more accurate and effective compared with the traditional motion target detection algorithm.

Citation Download Citation

Miao Peng, Peifeng Hao, Zijun Peng, and Caihua Yang "Strip steel target detection algorithm based on single image rapid defog and morphological interframe difference", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113732E (3 January 2020); https://doi.org/10.1117/12.2557250

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