Particle swarm optimization-based optimal real Gabor filter for surface inspection
Article publication date: 20 September 2019
Issue publication date: 18 October 2019
The traditional methods have difficulty to inspection various types of copper strips defects as inclusions, pits and delamination defects under uneven illumination. Therefore, this paper aims to propose an optimal real Gabor filter model for inspection; however, improper selection of Gabor parameters will cause the boundary between the defect and the background image to be not very clear. This will make the defect and the background cannot be completely separated.
The authors proposed an optimal Real Gabor filter model for inspection of copper surface defects under uneven illumination. This proposed method only requires a single filter by calculating the specific convolution energy of the Gabor filter with the image. The Real Gabor filter’s parameter is optimized by particle swarm optimization (PSO), which objective fitness function is maximization of the Gabor filter’s energy average divided by the energy standard deviation, the objective makes a distinction between the defect and normal area.
The authors have verified the effect with different iterations of parameter optimization using PSO, the effects with different control constant of energy and neighborhood window size of real Gabor filter, the experimental results on a number of metal surface have shown the proposed method achieved a well performance in defect recognition of metal surface.
The authors propose a defect detection method based on particle swarm optimization for single Gabor filter parameters optimization. This proposed method only requires a single filter and finds the best parameters of the Gabor filter. By calculating the specific convolution energy of the Gabor filter and the image, to obtain the best Gabor filter parameters and to highlight the defects, the particle swarm optimization algorithm’s fitness objective function is maximize the Gabor filter's average energy divided by the energy standard deviation.
This research was supported by the part by National Natural Science Foundation of China (No. 51505002, 51605004), Anhui Provincial Natural Science Foundation(1808085QE162).
Wu, H., Xu, X., Chu, J., Duan, L. and Siebert, P. (2019), "Particle swarm optimization-based optimal real Gabor filter for surface inspection", Assembly Automation, Vol. 39 No. 5, pp. 963-972. https://doi.org/10.1108/AA-04-2018-060
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