基于改进斑点鬣狗优化算法的红外图像分割

李唐兵; 胡锦泓; 周求宽

基于改进斑点鬣狗优化算法的红外图像分割

1.
国网江西省电力公司电力科学研究院，江西南昌 330096
2.
国网上海浦东供电公司，上海 200122

基金项目:

国网江西省电力公司科技项目 52182016001S

详细信息

作者简介:
李唐兵（1983-），男，高级工程师，研究方向电力设备故障诊断。E-mail：63463723@qq.com

中图分类号: TN219
计量
- 文章访问数: 208
- HTML全文浏览量: 62
- PDF下载量: 20
- 被引次数: 0
出版历程
- 收稿日期: 2020-11-23
- 修回日期: 2021-01-25
- 刊出日期: 2021-10-20

Infrared Image Segmentation Based on Improved Spotted Hyena Optimizer

1.
Power Science Research Institute of State Grid Jiangxi Electric Power Company, Nanchang 330096, China
2.
State Grid Shanghai Pudong Power Supply Company, Shanghai 200122, China

摘要

摘要: 针对斑点鬣狗优化算法（spotted hyena optimizer，SHO）容易陷入局部最优解、求解质量低等缺点，本文提出使用Lévy飞行和单纯形搜索算法改进SHO（spotted hyena optimizer based on simplex method and Lévy flight, Lévy_SM_SHO）。将Lévy_SM_SHO与Lévy飞行斑点鬣狗优化算法（spotted hyena optimizer based on Lévy flight, Lévy_SHO）、单纯形搜索斑点鬣狗优化算法（spotted hyena optimizer based on simplex method, SM_SHO）和SHO在测试函数上结果进行对比，实验证明改进算法能够取得较好的优化结果。并将Lévy_SM_SHO算法用于红外图像阈值分割问题，通过与粒子群算法（particle swarm optimization, PSO）分割结果对比，证明Lévy_SM_SHO算法能够取得较好的阈值分割结果。
- Lévy飞行 /
- 单纯形搜索算法 /
- Lévy_SM_SHO /
- Lévy_SHO /
- SM_SHO
Abstract: Based on the shortcomings of the spotted hyena optimizer (SHO), falling into a local optimal solution or a low-quality solution is easy. In this study, the Lévy flight and simplex method are proposed to improve the SHO(Lévy_SM_SHO). Comparing Lévy_SM_SHO to Lévy flight spotted hyena optimizer (Lévy_SHO), simplex method spotted hyena optimizer (SM_SHO), and spotted hyena optimizer (SHO) on the test function, the experiment proves that the improved algorithm can achieve better optimization results. Finally, the Lévy_SM_SHO algorithm is applied to the infrared image threshold segmentation problem. By crosschecking the segmentation results with the particle swarm optimization algorithm (PSO), we proved that the Lévy_SM_SHO algorithm can achieve better threshold segmentation results.
- lévy flight /
- simplex search algorithm /
- Lévy_SM_SHO /
- Lévy_SHO /
- SM_SHO

HTML全文

图 1 单纯形搜索法

Figure 1. Simplex search method

下载: 全尺寸图片幻灯片

图 2 四种算法对测试函数上的箱型图

Figure 2. Box diagrams of the four algorithms on the test function

下载: 全尺寸图片幻灯片

图 3 测试图像原图和灰度直方图

Figure 3. Original test images and gray histograms

下载: 全尺寸图片幻灯片

图 4 基于PSO-Otsu和Lévy_SM_SHO-Otsu算法的断路器瓷套二阈值分割结果

Figure 4. Two threshold segmentation results of ceramic sleeve of circuit breaker based on PSO-Otsu and Lévy_SM_SHO-Otsu algorithms

下载: 全尺寸图片幻灯片

图 5 基于PSO-Otsu和Lévy_SM_SHO-Otsu算法的断路器静触头二阈值分割结果

Figure 5. Two threshold segmentation results of circuit breaker static contacts based on PSO-Otsu and Lévy_SM_SHO-Otsu algorithms

下载: 全尺寸图片幻灯片

表 1 测试函数

Table 1. Test functions

Function	Expression	Dimension	Search range	Minimum
F₅	${f_{\rm{5}}}{\rm{(}}x{\rm{) = }}\sum\limits_{i{\rm{ = 1}}}^n {{\rm{[100(}}{x_{i{\rm{ + 1}}}} - {x_i}^{\rm{2}}{{\rm{)}}^{\rm{2}}}{\rm{ + (}}{x_i} - {\rm{1}}{{\rm{)}}^{\rm{2}}}{\rm{]}}} $	30	[-30, 30]	0
F₆	${f_6}(x) = \sum\limits_{i = 1}^n {{{(\|{x_i} + 0.5\|)}^2}} $	30	[-100, 100]	0
F₁₃	$\begin{gathered} {f_{13}}(x) = 0.1\{ {\sin ^3}(3{\rm{ \mathsf{ π} }}{x_1}) + \sum\limits_{i = 1}^n {{{({x_i} - 1)}^2}} [1 + {\sin ^2}(3{\rm{ \mathsf{ π} }}{x_i})] \\ \;\;\;\;\;\;\;\;\;\;\; + {({x_n} - 1)^2}[1 + {\sin ^2}(2{\rm{ \mathsf{ π} }}{x_n})]\} + \sum\limits_{i = 1}^n {u({x_i}, 5, 100, 4)} \\ \end{gathered} $ $u({x_i}, a, k, m) = \left\{ {\begin{array}{*{20}{c}} {k{{({x_i} - a)}^m}, \;\;\;{x_i} > a\;\;\;\;\;} \\ {0, \;\;\;\;\;\;\;\; - a \leqslant {x_i} \leqslant a\;\;} \\ {k{{( - {x_i} - a)}^m}, \;\;{x_i} < a\;\;} \end{array}} \right.$	30	[-50, 50]	0
F₁₆	${f_{16}}(x) = 4{x_1}^2 - 2.1{x_1}^4 + \frac{1}{3}{x_1}^6 + {x_1}{x_2} - 4{x_2}^2 + 4{x_2}^4$	2	[-5, 5]	-1.0316
F₁₇	${f_{17}}(x) = {({x_2} - \frac{{5.1}}{{4{{\rm{ \mathsf{ π} }}^2}}}{x_1}^2 + \frac{5}{{\rm{ \mathsf{ π} }}}{x_1} - 6)^2} + 10(1 - \frac{1}{{8{\rm{ \mathsf{ π} }}}})\cos {x_1} + 10\;\;\;\;\;\;\;\;\;\;\;$	2	[-5, 5]	0.398
F₂₀	${f_{20}}(x) = - \sum\limits_{i = 1}^4 {{c_i}} \exp ( - \sum\limits_{j = 1}^3 {{a_i}_j{{({x_j} - {p_{ij}})}^2}} )$	6	[0,1]	-3.3

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表 2 4种算法在测试函数上的测试结果

Table 2. Test results of 4 algorithms on test functions

Function	Indicators	SHO	Lévy_SHO	SM_SHO	Lévy_SM_SHO
F₅	Maximum	28.9838	29.6218	29	29.0713
	Minimum	28.7005	28.7027	28.5028	28.4771
	Average	28.8738	28.9371	28.7862	28.7253
	Standard	0.1025	0.0799	0.0947	0.1582
F₆	Maximum	7.06	7.02	7.50	6.19
	Minimum	0.326	0.0421	0.0182	0.003723
	Average	5.23	4.710	3.26	3.19
	Standard	1.93	2.29	2.92	2.25
F₁₃	Maximum	2.99	3.2709	3	3.0271
	Minimum	2.86	0.0046	2.7835	0.0023
	Average	2.95	1.8008	2.8493	1.6320
	Standard	0.0302	1.1731	0.0559	1.1147
F₁₆	Maximum	-0.11	-1.0101	0	-1.0094
	Minimum	-1.03	-1.0316	-1.0316	-1.0316
	Average	-0.94	-1.0252	-0.9882	-1.0304
	Standard	0.147	0.0057	0.1444	0.0031
F₁₇	Maximum	3.597	0.7162	0.51318	0.4690
	Minimum	0.398	0.3982	0.3979	0.3979
	Average	0.642	0.4282	0.4060	0.4031
	Standard	0.484	0.0528	0.0201	0.0108
F₂₀	Maximum	-1.6	-2.0591	-2.7087	-2.9141
	Minimum	-3.1	-3.0991	-3.3031	-3.3047
	Average	-2.6	-2.7685	-3.167	-3.1990
	Standard	0.3	0.2243	0.145831	0.09829

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表 3 基于PSO-Otsu和Lévy_SM_SHO-Otsu的最佳阈值

Table 3. The optimal thresholds based on PSO-Otsu and Lévy_SM_SHO-Otsu algorithms

Infrared image	The number of threshold	PSO-Otsu	Lévy_SM_SHO-Otsu
Infrared image	The number of threshold	threshold
Abnormal temperature distribution in porcelain sleeve of circuit breaker	1	70	70
	2	44, 145	46, 143
	3	73, 74, 255	70, 76, 255
	4	46, 138, 173, 255	46, 73, 144, 255
	5	69, 88, 181, 255, 255	46, 54, 132, 139, 255
	6	58, 142, 167, 181, 255, 255	53, 65, 76, 144, 255, 255
The circuit breaker still touches the hair to heat	1	63	63
	2	51, 147	57, 146
	3	44, 72, 255	63, 71, 255
	4	38, 80, 123, 255	37, 63, 121, 255
	5	58, 70, 135, 150, 255	55, 62, 72, 255, 255
	6	54, 71, 97, 186, 246, 255	58, 72, 82, 138, 245, 255

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表 4 基于PSO-Otsu和Lévy_SM_SHO-Otsu算法的适应度函数

Table 4. Fitness function based on PSO-Otsu and Lévy_SM_SHO-Otsu algorithms

Infrared image	The number of threshold	PSO-Otsu	Lévy_SM_SHO-Otsu
Infrared image	The number of threshold	The value of fitness functions
Load switch	1	851.7254	851.7254
	2	1096.1	1096.2
	3	1631.5	1633.5
	4	1746.8	1877
	5	2096.3	2197.8
	6	2255.8	2614.6
Load switch	1	1282.8	1282.8
	2	1486.6	1487
	3	2305.5	2362.6
	4	2521.7	2565.1
	5	2763.8	3437.9
	6	3182.6	3635.9

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表 5 基于PSO-Otsu和Lévy_SM_SHO-Otsu算法的PSNR和SSIM值

Table 5. PSNR and SSIM values based on PSO-Otsu and Lévy_SM_SHO-Otsu algorithms

Infrared image	The number of threshold	PSO-Otsu		Lévy_SM_SHO-Otsu
Infrared image	The number of threshold	PSNR	SSIM	PSNR	SSIM
Load switch	1	18.6778	0.0981	18.6778	0.0981
	2	21.0977	0.1753	21.1041	0.1794
	3	18.7711	0.0969	18.9324	0.1011
	4	21.7095	0.1741	22.3763	0.1806
	5	21.3552	0.1266	21.5929	0.1760
	6	21.3714	0.1370	21.9312	0.1580
Load switch	1	19.1335	0.1567	19.1335	0.1567
	2	20.7128	0.1686	21.2433	0.1720
	3	20, 0060	0.1738	20.1006	0.1741
	4	22.5955	0.1882	22.6117	0.2747
	5	22.6896	0.1892	22.7556	0.1908
	6	24.0231	0.1900	24.1233	0.1964

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