Abstract
Underwater image target detection is an important part of exploring the ocean. This paper adopts cascade classifier and image preprocessing method. Firstly, it selects candidate regions on a given picture, then extracts feature from them and finally uses the trained classifier to detect. It focuses on the self-defined training of the cascade classifier, and trains the cascade classifier by collecting a large number of underwater target images. Secondly, it uses some of image preprocessing to make the detection effect more accurate. Finally, the simulation results show that it can achieve the target detection of underwater image by using the method of self-defined cascade classifier and image preprocessing.
This project is supported by the National Natural Science Foundation of China (61873161, U1706224).
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
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR 2001, pp. 511–518 (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR 2005, pp. 1063–6919 (2005)
Felzenszwalb, P.F., Girshick, R.B., McAllester, D., et al.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32(9), 1627–1645 (2010)
Freund, Y., Schapire, R.E.: A short introduction to boosting. J. Jpn. Soc. Artif. Intell. 14(5), 771–780 (1999)
Li, X., Wang, L., Sung, E.: AdaBoost with SVM-based component classifiers. Eng. Appl. Artif. Intell. 21(5), 785–795 (2008)
Zhang, L., Chu, R., Xiang, S., Liao, S., Li, S.Z.: Face detection based on multi-Block LBP representation. In: Lee, S.-W., Li, S.Z. (eds.) ICB 2007. LNCS, vol. 4642, pp. 11–18. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74549-5_2
Tran, C.K., Lee, T.F., Chang, L., et al.: Face description with local binary patterns and local ternary patterns: improving face recognition performance using similarity feature-based selection and classification algorithm. In: International Symposium on Computer, pp. 520–524 (2014)
Zhou, J., Peng, F.: A method of selective image graying. Comput. Eng. 32(20), 198–200 (2006)
Yelmanova, E., Romanyshyn, Y.: Histogram-based method for image contrast enhancement. In: Experience of Designing & Application of Cad Systems in Microelectronics, pp. 165–169 (2017)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Publishing House of Electronics Industry (2007)
Ye, Y., Sun, Q., Shi, L., et al.: A adaptive dual-platform deep space infrared image enhancement algorithm based on linear gray scale transformation. In: Chinese Control Conference, pp. 7434–7438 (2014)
Yamasaki, I., Ohshima, T., Hasegawa, M., et al.: Segmentation of gray-scale images using piecewise linear approximation. Syst. Comput. Jpn. 27(1), 69–76 (1996)
Tafti, A.D., Mirsadeghi, E.: A novel adaptive Recursive Median Filter in image noise reduction based on using the entropy. In: IEEE International Conference on Control System, pp. 520–523 (2013)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zeng, L., Sun, B., Zhang, W., Zhu, D. (2019). Underwater Image Target Detection with Cascade Classifier and Image Preprocessing Method. In: Yu, H., Liu, J., Liu, L., Ju, Z., Liu, Y., Zhou, D. (eds) Intelligent Robotics and Applications. ICIRA 2019. Lecture Notes in Computer Science(), vol 11742. Springer, Cham. https://doi.org/10.1007/978-3-030-27535-8_18
Download citation
DOI: https://doi.org/10.1007/978-3-030-27535-8_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-27534-1
Online ISBN: 978-3-030-27535-8
eBook Packages: Computer ScienceComputer Science (R0)