光学学报, 2020, 40 (16): 1610003, 网络出版: 2020-08-07   

基于文化狼群算法的电力设备红外和可见光图像配准 下载: 890次

Power Equipment Infrared and Visible Images Registration Based on Cultural Wolf Pack Algorithm
作者单位
华北电力大学电气与电子工程学院, 河北 保定 071003
摘要
可见光和红外图像是电力巡检机器人检测电力设备健康状态的重要方式,图像配准可以结合两类图像的优势,为后续状态监测提供更好的依据。针对红外图像模糊导致的配准精度下降问题,提出了一种基于显著性梯度的归一化互信息算法。首先,在红外图像视觉显著性检测的基础上,强化了显著性区域的边缘梯度信息;然后,将显著性梯度信息和归一化互信息相结合作为配准的测度函数;其次,为了提高图像配准算法的收敛性,提出了一种文化狼群算法。该算法将文化算法的分层进化特点引入狼群算法,建立信念空间和群体空间。在迭代过程中,通过信念空间的知识指导群体空间的进化。最后,选取变电站巡检图像、标准配准测试图像集和标准测试函数进行对比实验,结果表明,该算法在配准率和配准速度方面的性能较好。
Abstract
Visible and infrared images are important ways for power inspection robot to detect the health status of power equipment. Image registration can combine the advantages of two types of images and provide a better basis for subsequent status monitoring. To improve the registration accuracy due to the blur of infrared image, this paper proposes a normalized mutual information algorithm based on saliency gradient. First, based on the visual saliency detection of the infrared image, the edge gradient information of saliency area is enhanced. Second, the saliency gradient information and normalized mutual information are combined as a measurement function of registration. Third, to improve the convergence of the image registration algorithm, a cultural wolf pack algorithm is proposed. This algorithm introduces the hierarchical evolutionary characteristics of cultural algorithm into the wolf pack algorithm to establish the belief space and population space. In the iterative process, the evolution of population space is guided by the knowledge of belief space. Finally, the substation inspection image, standard registration test image set, and standard test functions are selected for comparative experiments. The results show that the proposed algorithm has better performance in registration rate and registration speed.

赵洪山, 张则言. 基于文化狼群算法的电力设备红外和可见光图像配准[J]. 光学学报, 2020, 40(16): 1610003. Hongshan Zhao, Zeyan Zhang. Power Equipment Infrared and Visible Images Registration Based on Cultural Wolf Pack Algorithm[J]. Acta Optica Sinica, 2020, 40(16): 1610003.

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