摘要
为了清晰快速地辨识飞机机体损伤区域,引入K均值聚类算法处理损伤图像,通过分析K均值聚类算法的局限性,提出了基于误差平方和差值以及基于像素点变化的聚类迭代终止条件的优化方法,并通过飞机机体损伤图像实例,从损伤识别效果和运算效率两个方面进行验证。验证结果显示,基于改进聚类的机体损伤识别方法,在保证图像损伤区域识别效果的同时,明显减少了迭代次数,提高了聚类算法的运算效率,可以满足飞机机体损伤高效识别的要求。
In order to identify the airframe damage region clearly and quickly,the K-means clustering algorithm is introduced to process the damage image.By analyzing the limitations of the K-means clustering algorithm,an improved method of clustering iteration termination condition based on error square sum difference and pixel change is proposed.Through the verification of aircraft body damage images,it is verified from two aspects:damage recognition effect and operation efficiency.The verification results show that the airframe damage identification method based on improved clustering not only ensures the identification effect of image damage regions,but also significantly reduces the number of iterations,improves the operation efficiency of the clustering algorithm,and can meet the requirements of efficient processing of airframe damage identification.
作者
范杰
司伟森
巴翔
Fan Jie;Si Weisen;Ba Xiang(China Southern Airlines Henan Branch,Zhengzhou 450000,China)
出处
《航空科学技术》
2022年第5期32-36,共5页
Aeronautical Science & Technology
关键词
损伤区域划分
图像识别
K均值聚类算法
damage region division
image recognition
K-means clustering algorithm
