Abstract: Aiming at the low efficiency of magnetic column surface defect detection, the vulnerability to human influence, and the insufficient anti-noise performance of existing 2D-OTSU threshold segmentation algorithm, an improved artificial fish swarm algorithm combined with 2D-OTSU algorithm was proposed to improve the accuracy and real-time of magnetic column surface defect detection. Firstly, the weight coefficient was added on the basis of the original 2D-OTSU algorithm, and the distance function was set to optimize the weight coefficient. The objective function was established by combining the inter-class discrete matrix and the intra-class discrete matrix, and the optimal threshold was obtained. Secondly, logistic model was used to optimize the perceptual range and moving step size of the artificial fish swarm algorithm, so as to balance the local and global search ability of the algorithm and improve the convergence speed of the algorithm. Finally, the optimal segmentation threshold is used to segment the image, and compared with other algorithms on four benchmark functions. Experimental results show that the improved algorithm can effectively reduce the time complexity of threshold segmentation and improve the efficiency of the algorithm. At the same time, the segmentation accuracy of the improved algorithm for magnetic column defects reaches 93%, which has good practicability.

Wang Jun, Hou Mengjie, Zhang Ruiran and Xiao Jingjing, “Threshold Segmentation of Magnetic Column Defect Image based on Artificial Fish Swarm Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 13(6), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130661

@article{Jun2022,
title = {Threshold Segmentation of Magnetic Column Defect Image based on Artificial Fish Swarm Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130661},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130661},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {6},
author = {Wang Jun and Hou Mengjie and Zhang Ruiran and Xiao Jingjing}
}