ICF准直系统的非正常图像分类检测

Study on classification and detection method for off-normal images in ICF automatic alignment system

摘要:

为克服由于非正常图像的存在,导致准直循环次数增加,降低了准直的效率这一困难,对非正常图像的特点进行了分析,设计了贝叶斯分类器实现对图像的分类,并通过对光斑图像形状因子的判定实现对畸变图像的检测.实验结果表明:通过设定合理的分类条件及形状判定标准,可以有效地实现对非正常图像的分类、检测过滤,减少基于非正常图像的准直循环次数,降低因非正常图像导致准直失败的概率,有效改善自动准直系统的工作效率;同时对非正常图像的产生原因进行初步分类,为故障的快速定位提供支持.

Abstract:

Due to off-normal images,the number of alignment iterations will be increased and the collimation efficiency will be decreased.To overcome this difficulty,by analyzing the characteristics of off-normal images,a Bayesian classifier is designed to achieve image classification,and the distortion images are detected by the shape factor of beam image.Experimental results show that: by setting reasonable class condition and shape determination standard,the off-normal image classification and examination filtration can be effectively realized.As a consequence,the collimation cycle-index and alignment defeat′s probability due to the off-normal images is decreased,i.e.the total alignment efficiency is dramatically increased.In addition,the preliminary reason causing the off-normal images is analyzed,which supports the breakdown fast localization.