Study on Defects Edge Detection in Infrared Thermal Image based on Ant Colony Algorithm

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1 , pp Study on Defects Edge Detection in Infrared Thermal Image based on Ant Colony Algorithm Tang Qingju 1,2, Dai Jingmin 1, Liu Chunsheng 2, Liu Yuanlin 2, Ren Chunping 2 1 School of Electrical Engineering and Automation, Harbin Institute of Technology, Harbin, , P.R. China 2 School of Mechanical Engineering, Heilongjiang University of Science and Technology, Harbin, , P.R. China tangqingju@126.com Abstract. Edge extraction is an important part in the detection of infrared thermal images. Ant colony algorithm has the characteristics of high efficiency, high noise suppression, and comprehensive information of edge information. The basic principle of ant colony algorithm is analyzed. An ant colony optimization algorithm for image edge detection is established. And to have defective parts for analysis of infrared thermography The ant colony algorithm and the classical Canny operator are compared and analyzed. The experimental results show that the algorithm has high efficiency, comprehensive information and high computational efficiency. Keywords: Infrared thermography; Defect edge detection; Ant colony algorithm 1 Introduction Edge is one of the most simple and important image features, including a large amount of image information, which has been widely used in the field of image recognition, segmentation, enhancement, compression and so on. Edge exists in the target and background, target and target, region and region, is the image of the object structure, texture, shape of important information, edge detection for image postprocessing and analysis provides an important parameters, the follow-up to further feature description, matching and recognition, etc. have a significant impact on [1-3]. In addition, ant colony algorithm is applied to image edge detection in recent years because of its advantages, such as high computational speed, high efficiency of noise reduction, complete extraction of edge information. 2 Ant Colony Algorithm Ant colony algorithm is a kind of optimization algorithm proposed by Italy scholar M Dorigo in It is a kind of random search method with advantages of discrete, ISSN: ASTL Copyright 2016 SERSC

2 parallel and robust [4-6]. Ant colony algorithm is a kind of simulated evolutionary algorithm, which is based on the behavior of ants in searching for food. The discrete and parallel characteristics of ant colony algorithm has certain advantages in processing digital images. The genetic algorithm flow chart is shown in Fig. 1. Start Initialize ant position and pheromone Ant path building Pheromone local updating Is a cycle over? N Y Find out the optimal solution Pheromone global Update Iterative terminated? N End Y Fig.1. Genetic algorithm flow chart 3 Image Edge Detection Algorithm based on Ant Colony Optimization 3.1 Initial Ant Distribution In general, the number of ants is set to K M N ( M and N length and width of the picture respectively). denonte the 38 Copyright 2016 SERSC

3 3.2 Transfer Probability The probability that the ants from the pixel i, j to the pixel kl, is w Pk, l= k, l k, l k, l,, w, j k l k l k l, kl, (1) 3.3 Transfer Rule Ants move from pixels to the rules of the pixels. max, k l k, l w k, l arg s kl, S Where, q is a random and q 0,1, random. q q 0 else (2) q is a preset value; S is a selected Pheromone Update When ants move from pixel i, j to pixel kl,, the pheromone updates rule is as follows Where, 0,1 1 k, l k, l k, l k, l k, l indicates that pheromone volatile factor. (3) 3.5 Edge Extraction After the completion of the set of iterations, the choice of a threshold, according to the intensity of each pixel of the pheromone intensity to determine whether the point is the edge point 4 Experimental Simulation and Analysis In this paper, the defects edge of infrared thermal image is extracted. Fig.2 shows a thermal image captured the infrared camera. Fig.3 shows the image after gray level Copyright 2016 SERSC 39

4 transformation. Using Canny edge detection operator and ant colony algorithm to extract the edge of the image. Specific results are shown in Fig. 4 and Fig. 5. Fig. 2. The input infrared image Fig. 3. The image after gray level transformation Fig. 4. Processed by canny operator Fig. 5. Processed by ant ant colony algorithm The experimental results show that the algorithm has the characteristics of high efficiency, strong ability of restraining the noise and the edge information, and can effectively extract the real edge of the object. 5 Conclusion Based on the analysis of the principle of ant colony algorithm, the image edge detection method based on ant colony algorithm is discussed. The comparison of canny and ant colony algorithm is carried out. The results show that the ant colony algorithm has the advantages of high efficiency, comprehensive accuracy of edge information in extracting the edge of infrared image. Acknowledgments. This project is supported by Youth Innovation Talent Training Program of Heilongjiang Province Regular Institutions of Higher Education Study on CFRP laminate defects detection using infrared thermal wave nondestructive testing technology under modulated laser exitation, Heilongjiang Province Natural Science Fund (Grant No. QC2013C054), and Cooperation Project between Heilongjiang Province and Chinese Academy of Science (Grant No. YS15A10). 40 Copyright 2016 SERSC

5 References 1. Mallat, S., Zhong, S.: Characterizations of Signals from Multiscale Edges, J. IEEE Trans. On Pattern Analysis and Machine Intelligence.14, (1992) 2. Lopez, Molina C., De Baets B., Bustince H.: Multiscale edge detection based on gaussian smoothing and edge tracking, J. Knowledge Based Systems.44, (2013) 3. Nes, P G.: Fast multi-scale edge detection in medical ultrasound signals, J. Signal Processing.92, (2012) 4. Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization: artificial ants as a computational intelligence technique, J. IEEE Computational Intelligence Magazine. 11,28-39 (2006) 5. Mullen, R J., Monekosso, D N., Remagnino, P.: Ant algorithms for image feature extraction,j. Expert Systems With Applications. 40, (2013) 6. Zhang, W., Pan, X., Liu, Z.: Manufacturing service scheduling strategy on cloud model ant colony optimization, J. Computer Integrated Manufacturing Systems. 18, (in Chinese) (2012) Copyright 2016 SERSC 41