基于2维鲸鱼优化加权的WGG-Otsu算法的钢板表面缺陷图像分割

Steel plate surface defect image segmentation based on two-dimensional weighted WGG-Otsu with whale optimization

相对于1维Otsu算法,2维灰度级-梯度Otsu算法的抗噪声能力有所增强,但对受噪声影响较大的钢板表面缺陷图像的分割精度仍不高,且分割效率较低.为提高钢板表面缺陷图像分割的精度和效率,提出2维鲸鱼优化加权的灰度级-梯度Otsu算法.2维鲸鱼优化加权的灰度级-梯度Otsu算法能增大分布概率较低的阈值类间方差,减少背景与目标间差异对分割的影响.仿真实验结果表明,4种算法中,2维鲸鱼优化加权的灰度级-梯度Otsu算法的分割精度及效率均最高.

Compared with the one-dimensional Otsu algorithm,the two-dimensional grayscale-gradient Otsu algorithm had better anti-noise ability.However,the segmentation accuracy was still insufficient and the segmentation efficiency was low for the steel plate surface defect image which was greatly affected by noise.In order to improve the accuracy and efficiency of plate surface defect image segmentation,the two-dimensional weighted grayscale-gradient Otsu algorithm with whale optimization was proposed.The two-dimensional weighted grayscale-gradient Otsu algorithm with whale optimization increased the inter-class variance of threshold with lower distribution probability,and reduced the influence of the large difference between the background and the target on segmentation.The simulation results showed that the segmentation accuracy and efficiency of the two-dimensional weighted grayscale-gradient Otsu algorithm with whale optimization were the highest among the four algorithms.