Infrared Image Adaptive Canny Edge-detection of Aircraft Skin Based on Fast Fireworks Algorithm
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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。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.
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Keywords:
- edge-detection /
- fireworks algorithm /
- Canny algorithm /
- Otsu /
- adaptive
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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 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
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=0.81 0.99 FWA 2 IFWA 2 21 CFWA 2 0.15 dynFWA kd=1.2
sx=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 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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