改进粒子群优化的铁谱图像聚类分割  

Clustering Segmentation of Ferrograph Images Based on Improved Particle Swarm Optimization

作　　者：李浩天 戴乐阳[1,2] 王永坚 宋佳声[1,2] LI Haotian;DAI Leyang;WANG Yongjian;SONG Jiasheng(School of Marine Engineering,Jimei University,Xiamen 361021,China;Fujian Provincial Key Laboratory of Naval Architecture and Ocean Engineering,Xiamen 361021,China)

机构地区：[1]集美大学轮机工程学院,福建厦门361021 [2]福建省船舶与海洋工程重点实验室,福建厦门361021

出　　处：《集美大学学报(自然科学版)》2025年第1期95-102,共8页Journal of Jimei University:Natural Science

基　　金：国家自然科学基金项目“等离子体耦合球磨制备石墨烯基纳米复合润滑添加剂及其摩擦学性能”(51779103);福建省自然科学基金项目“在线铁谱图像分析系统中磨粒的沉降控制与快速分割算法研究”(2020J01686)、“抑制低硫条件下船舶柴油机缸套腐蚀磨损的利润添加剂的研究”(2021J01848)。

摘　　要：在采用K均值聚类算法对铁谱图像进行分割时,由于初始聚类中心的随机性和铁谱图像的复杂性,常出现误分割现象,影响后续铁谱分析的效果。为了解决这一问题,采用基于归一化RGB颜色模型,引入模拟退火优化的自适应粒子群算法,对K均值聚类方法进行优化,可以有效缓解误分割问题,并取得全局最优搜索能力和收敛速度之间的平衡。实验结果表明,改进算法提高了铁谱图像的分割精度,并保证了运行效率。When using K-means clustering algorithm to segment ferrography images,due to the randomness of initial clustering center and the complexity of ferrography image,missegmentation will occur,which will affect the effectiveness of subsequent ferrography analysis.In order to solve the problem,based on the normalized RGB color model,the K-means clustering method is optimized by introducing the simulated annealing optimization adaptive particle swarm optimization algorithm,which can effectively alleviate the problem of missegmentation.At the same time,the algorithm can achieve a balance between the global optimal solution searching ability and the convergence speed.The experimental results show that the improved algorithm improves the segmentation accuracy of ferrograph images and ensures operational efficiency.

关 键 词：铁谱图像分割 聚类 粒子群优化 模拟退火 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]