基于改进黏菌算法与Tsallis熵的电力设备红外图像分割  

Infrared Image Segmentation of Electrical Equipment Based on Improved Slime Mould Algorithm and Tsallis Entropy

作　　者：赵文博 向东 王玖斌 邓岳辉 张伟 康倩 李玉洁 ZHAO Wenbo;XIANG Dong;WANG Jiubin;DENG Yuehui;ZHANG Wei;KANG Qian;LI Yujie(Huaneng Chongqing Liangjiang Gas Turbine Power Generation Co.,Ltd.,Chongqing 400799,China;Huaneng Power Internationnal,Inc.Chongqing Branch,Chongqing 401120,China;Beijing Levcn Electric Technology Corporation Limited,Beijing 100085,China)

机构地区：[1]华能重庆两江燃机发电有限责任公司,重庆400799 [2]华能重庆分公司,重庆401120 [3]北京中电方大科技有限公司,北京100085

出　　处：《计算机与现代化》2024年第6期70-75,120,共7页Computer and Modernization

基　　金：国家自然科学基金资助项目(61702140)。

摘　　要：常规方法处理电力设备红外图像分割问题时,求解最优阈值容易出现分割精度差、计算效率低的不足。为此,本文提出基于改进黏菌算法优化Tsallis熵的多阈值红外图像分割方法。利用黏菌算法的启发式搜索机制求解图像分割最优阈值,有效降低算法时间复杂度。在传统黏菌算法中引入Henon混沌映射优化初始种群多样性,设计动态透镜成像对立学习机制提高算法搜索精度。以Tsallis熵评估黏菌个体的适应度改进黏菌算法,迭代搜索图像分割阈值最优解。在常规电力设备红外图像数据集上进行实验,结果表明:与对比模型相比,改进模型具有更低的误分率和更高的峰值信噪比与结构相似度,在处理背景非均匀、噪声较大的红外图像分割上具有性能优势。When using conventional methods to deal with infrared image segmentation of electrical equipment,it is easy to have the shortcomings of poor segmentation accuracy and low computational efficiency in determing the optimal threshold.Therefore,a multi-threshold infrared image segmentation method based on improved slime mold algorithm optimizing Tsallis entropy is proposed.The optimal threshold of image segmentation is determined by using the heuristic search mechanism of slime mold algorithm to effectively reduce the time complexity of the algorithm.In the traditional slime mold algorithm,Henon chaotic mapping is introduced to optimize the initial population diversity,and a dynamic lens imaging opposite learning mechanism is designed to improve the search accuracy of the algorithm.Tsallis entropy is used to evaluate the quality of slime mold individuals,and an improved slime mold algorithm iteratively searches for the optimal image segmentation threshold.We construct experimental analysis using a common infrared image dataset of electrical equipment.The results show that compared with contrast model,the segmentation model achieves lower misclassification error,higher peak signal-to-noise ratio and structural similarity degree.The improved model demonstrates performance advantages in processing infrared image segmentation with non-uniform background and high noise.

关 键 词：黏菌算法 红外图像分割 图像熵 Henon混沌 透镜成像 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]