基于FFWA的自适应Canny飞机蒙皮红外图像边缘检测

王坤; 刘沛伦; 王力

基于FFWA的自适应Canny飞机蒙皮红外图像边缘检测

中国民航大学，电子信息与自动化学院，天津 300300

基金项目:

国家自然科学基金 U1733119

中央高校基本科研业务费项目中国民航大学专项 3122018C001

详细信息

作者简介:
王坤（1978-），女，博士，副教授，主要研究方向为图像处理、故障检测分析。E-mail：96288851@qq.com

中图分类号: TP274.52
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- PDF下载量: 51
- 被引次数: 0
出版历程
- 收稿日期: 2020-06-06
- 修回日期: 2020-07-13
- 刊出日期: 2021-05-22

Infrared Image Adaptive Canny Edge-detection of Aircraft Skin Based on Fast Fireworks Algorithm

College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China

摘要

摘要: 针对传统自适应Canny算法阈值选取精度低、速度慢的不足，提出基于快速烟花算法（Fast Fireworks Algorithm，FFWA）的自适应Canny边缘检测算法，该算法采用最大类间方差法结合快速烟花算法对检测和连接边缘的高低阈值进行自动设定。快速烟花算法对传统烟花算法的爆炸半径，爆炸火花产生方式与选择策略进行改进。实验结果表明，快速烟花算法比传统烟花算法的计算时间节省了36%，在稳定性方面也有了可观的提升，基于快速烟花算法的自适应Canny边缘检测算法在精度保持不变的情况下，计算速度比改进前加快了49%，使飞机蒙皮损伤热像图的边缘检测效果更加理想。
- 边缘检测 /
- 烟花算法 /
- Canny算法 /
- 最大类间方差法 /
- 自适应
Abstract: To address the low accuracy and slow speed of the traditional adaptive Canny algorithm in selecting the threshold value, an improved algorithm is herein proposed. The proposed algorithm utilizes the Otsu and fast fireworks algorithm (FFWA) to automatically set the high and low detection thresholds and the connecting edges. Consequently, the explosion radius, the production method, and selection strategy are improved as compared to those of the traditional fireworks algorithm, thereby increasing the speed and accuracy of the Otsu calculation. The experimental results show that the calculation speed of the fast fireworks algorithm increased by 36% as compared to that of the traditional fireworks algorithm. Moreover, the stability also increased considerably. The improved adaptive Canny algorithm not only maintains the same precision but also decreases the calculation time by 49%. This makes the result of edge detection of aircraft skin infrared images more ideal.
- edge-detection /
- fireworks algorithm /
- Canny algorithm /
- Otsu /
- adaptive

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图 1 烟花爆炸示意图

Figure 1. Schematic diagram of fireworks explosion

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图 2 传统烟花算法流程图

Figure 2. Flowchart of traditional Fireworks Algorithm

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图 3 FFWA在Sphere函数上自适应爆炸半径的取值

Figure 3. The value of adaptive explosion radius Of FFWA in Sphere function

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图 4 快速烟花算法流程图

Figure 4. Fast fireworks algorithm flow chart

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图 5 基于FFWA的自适应Canny边缘检测算法流程图

Figure 5. Flowchart of adaptive Canny edge detection algorithm based on FFWA

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图 6 Sphere函数收敛曲线

Figure 6. Sphere function convergence curves

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图 7 Ackley函数收敛曲线

Figure 7. Ackley function convergence curves

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图 8 Griewank函数收敛曲线

Figure 8. Griewank function convergence curves

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图 9 Six-hump函数收敛曲线

Figure 9. Six-hump function convergence curves

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图 10 积水损伤加热结束后不同时刻图像

Figure 10. Images of different moments of hydrops

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图 11 脱粘损伤加热结束后不同时刻图像

Figure 11. Images of different moments of debonding

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图 12 裂痕损伤加热结束后不同时刻图像

Figure 12. Images of different moments of crack

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图 13 腐蚀损伤加热结束后不同时刻图像

Figure 13. Images of different moments of Corrosion

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图 14 积水损伤红外热像图阈值计算的收敛曲线

Figure 14. Convergence curves of hydrops

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图 15 脱粘损伤红外热像图阈值计算的收敛曲线

Figure 15. Convergence curves of debonding

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图 16 飞机蒙皮积水损伤边缘检测结果

Figure 16. Edge detection results of hydrops

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图 17 飞机蒙皮脱粘损伤边缘检测结果

Figure 17. Edge detection results of debonding

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图 18 飞机蒙皮裂痕损伤边缘检测结果

Figure 18. Edge detection results of cracks

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图 19 飞机蒙皮腐蚀损伤边缘检测结果

Figure 19. Edge detection results of corrosion

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表 1 基准测试函数

Table 1. Standard test function

Name	Function	Range	Dimension	Optimal
Sphere	${f_1}\left( x \right) = \sum\limits_{i = 1}^n {x_i^2} $	[-100, 100]^d	30	0
Ackley	${f_2}(x) = - 20\exp ( - 0.2\sqrt {\sum\limits_{i = 1}^d {x_i^2} } ) - \exp (\frac{1}{d})\sum\limits_{i = 1}^d {\cos (2{\rm{ \mathsf{ π} }}{x_i})} + 20 - e$	[-32, 32]^d	30	0
Griewank	${f_3}(x) = \frac{1}{{4000}}\sum\limits_{i = 1}^d {x_i^2} - \prod\limits_{i = 1}^d {\cos (\frac{{{x_i}}}{{\sqrt i }})} + 1 $	[-600, 600]^d	30	0
Six-Hump Camel-Black	${f_4}(x) = 4x_1^2 - 2.1x_1^4 + \frac{1}{3}x_1^6 + {x_1}{x_2} - 4x_2^2 + 4x_2^4 $	[-5, 5]²	2	-1.03162

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表 2 五种算法的参数设置

Table 2. Parameter Settings

Algorithm	Common Parameters	Mutations	Acceptance probability	Shrinkage Rate	Transfer parameters	Control factor	Mutation probability
FFWA	N=5 Er=20 En=20 a=0.2 b=0.8		1	0.99
FWA		2
IFWA		2			21
CFWA		2					0.15
dynFWA						k_d=1.2 s_x=0.8

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表 3 测试函数测试结果对比

Table 3. Test function test results comparison

Algorithm	Fun	Optimal	Worst	Average	Run Time/s	Fun	Optimal	Worst	Average	Run Time/s
FFWA	f₁	0	0	0	0.85	f₃	0	1.078×10^-5	2.501×10^-5	1.04
FWA		0	2.887×10^-2	5.559×10^-5	1.33		9.716×10^-5	7.410×10^-1	2.552×10^-5	1.50
dynFWA		1.636×10^-4	1.222	1.328×10^-3	1.06		3.414×10^-4	1.053	5.513×10^-3	1.19
IFWA		4.584×10^-1	4.839×10^-3	4.837×10^-2	1.28		1.113E-01	6.591×10^-1	4.447×10^-1	1.51
CFWA		2.032×10^-5	2.996×10^-1	2.842×10^-5	1.25		0	2.285×10^-1	4.311×10^-4	1.46
FFWA	f₂	1.216×10^-5	1.078×10^-1	2.601×10^-4	1.03	f₄	-1.031628	-1.031628	-1.031628	0.69
FWA		1.215×10^-5	1.880×10^-1	2.634×10^-4	1.51		-1.031628	-1.031613	-1.031620	1.04
dynFWA		5.001×10^-4	4.203	2.611×10^-3	1.27		-1.031628	0.007603	-1.031614	0.87
IFWA		2.093×10^-4	1.666	4.537×10^-4	1.46		-1.031628	-1.031628	-1.031628	0.96
CFWA		8.282×10^-5	6.323×10^-4	2.220×10^-4	1.46		-1.031628	-1.031624	-1.031626	1.03

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表 4 各个算法的实验结果

Table 4. The experimental results of each algorithm

		Mean Convergence Time/s				Total Error				Average number of iterations
		Hyd	Deb	Cra	Cor	Hyd	Deb	Cra	Cor	Hyd	Deb	Cra	Cor
t=20 s	FFWA	1.116	1.112	1.116	1.112	0	0	1	0	4.5	4	5	4.5
	FWA	1.752	1.750	1.752	1.750	9	11	9	10	6.5	6.5	6	6
	dynFWA	1.429	1.411	1.429	1.411	4	2	4	3	5	5	6	5
	IFWA	1.644	1.640	1.644	1.640	0	1	2	1	5	4.5	5	5
	CFWA	1.539	1.537	1.539	1.537	0	2	0	1	5	5	5.5	4
t=100 s	FFWA	1.100	1.115	1.100	1.115	0	1	0	1	4	4.5	4	5
	FWA	1.771	1.690	1.771	1.690	10	9	9	7	6	6	5	6
	dynFWA	1.430	1.381	1.430	1.381	1	2	4	3	5	5	5	6
	IFWA	1.644	1.640	1.644	1.640	1	0	0	2	5	4.5	4	5
	CFWA	1.539	1.537	1.539	1.537	0	0	2	4	5	6	5	4.5
t=200 s	FFWA	1.110	1.109	1.112	1.110	0	1	0	1	5	5	4.5	6
	FWA	1.746	1.742	1.757	1.751	5	7	9	10	6.5	6	5	7
	dynFWA	1.427	1.408	1.423	1.412	1	4	4	6	5	5	4.5	4.5
	IFWA	1.647	1.639	1.645	1.640	0	1	0	2	5	4.5	4	4
	CFWA	1.539	1.537	1.537	1.537	1	0	0	1	5	5	5	4.5

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表 5 4种算法检测对比结果

Table 5. The comparison results of four algorithms

Algorithm	Run Time/s				Threshold				PFOM
Algorithm	Hyd	Deb	Cra	Cor	Hyd	Deb	Cra	Cor	Hyd	Deb	Cra	Cor
Proposed method	2.18	2.18	2.06	2.13	133	137	144	96	0.83	0.96	0.87	0.81
Global threshold method	3.15	3.15	3.12	3.20	139	120	130	112	0.51	0.74	0.76	0.76
Maximum entropy method	4.59	4.58	4.51	5.02	120	120	182	80	0.70	0.96	0.83	0.85
Otsu method	4.33	4.29	4.30	4.17	133	137	144	96	0.83	0.96	0.87	0.81

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