Abstract
A hybrid model, combining the Division Pigeon-inspired Optimization (DPIO) with a novel target detection method which is based on K-means and Edge Potential Function (K-EPF), is proposed in this paper. In K-EPF, K-mean is used to segment the image into two parts, which is helpful to enhance the efficiency of shape-matching. Basic PIO algorithm is prone to falls into local optima. In DPIO algorithm, this problem is solved by the multi-population mechanism and the landmark operator based on elite list. It effectively improves the optimization performance and convergence speed of the algorithm. In order to prove the superiority of DPIO, a series of algorithm is utilized in our comparative experiments, including particle swarm optimization (PSO), and standard genetic algorithm (GA).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Lygouras, E., Gasteratos, A., Tarchanidis, K., Mitropoulos, A.: ROLFER: a fully autonomous aerial rescue support system. Microprocess. Microsyst. 61, 32–42 (2018)
Li, C., Duan, H.: Target detection approach for UAVs via improved pigeon-inspired optimization and edge potential function. Aerosp. Sci. Technol. 39, 352–360 (2014)
Dao, M.S., De, N., Massa, A.: Edge potential functions (EPF) and genetic algorithms (GA) for edge-based matching of visual objects. IEEE Trans. Multimed. 9(1), 120–135 (2007)
Moradi, M.H., Abedini, M.: A combination of genetic algorithm and particle swarm optimization for optimal DG location and sizing in distribution systems. Int. J. Electr. Power Energy Syst. 34(1), 66–74 (2012)
Karaboga, D., Okdem, S., Ozturk, C.: Cluster based wireless sensor network routing using artificial bee colony algorithm. Wirel. Netw. 18(7), 847–860 (2012)
Duan, H.B., Qiao, P.X.: A new swarm intelligence optimizer for air robot path planning. Int. J. Intell. Comput. Cybern. 7(1), 24–37 (2014)
Mallipeddi, R., Suganthan, P.N., Pan, Q.K., Tasgetiren, M.F.: Differential evolution algorithm with ensemble of parameters and mutation strategies. Appl. Soft Comput. 11(2), 1679–1696 (2011)
Meng, D., Cao, Y., Guo, L.: A method to recognize and track runway in the image sequences based on template matching. In: 1st International Symposium on Systems and Control in Aerospace and Astronautics, pp. 1218–1221. IEEE Press, New York (2006)
Deng, Y.M., Duan, H.B.: Biological edge detection for UCAV via improved artificial bee colony and visual attention. Aircr. Eng. Aerosp. Technol. 86(2), 138–146 (2014)
Niu, Y., Xu, S., Wu, L., Hu, W.: Airborne infrared and visible image fusion for target perception based on target region segmentation and discrete wavelet transform. Math. Prob. Eng. (2012). https://doi.org/10.1155/2012/275138
Acknowledgment
This work is partially supported by The National Natural Science Foundation of China (Grants Nos. 71571120, 71271140, 61472257). Lu Xiao, and Jinsong Chen contributed equally to this work and shared the first authorship.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Chen, J., Xiao, L., Wang, J., Liu, H., Liu, Q. (2018). A Hybrid Model Based on K-EPF and DPIO for UAVs Target Detection. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 952. Springer, Singapore. https://doi.org/10.1007/978-981-13-2829-9_29
Download citation
DOI: https://doi.org/10.1007/978-981-13-2829-9_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2828-2
Online ISBN: 978-981-13-2829-9
eBook Packages: Computer ScienceComputer Science (R0)