A Comparative Study of Various Edge Detection Techniques for Underwater Images
DOI:
https://doi.org/10.26636/jtit.2022.155921Keywords:
edge detection, mean square error, median filtering, peak signal to noise, wiener filteringAbstract
Nowadays, underwater image identification is a challenging task for many researchers focusing on various applications, such as tracking fish species, monitoring coral reef species, and counting marine species. Because underwater images frequently suffer from distortion and light attenuation, pre-processing steps are required in order to enhance their quality. In this paper, we used multiple edge detection techniques to determine the edges of the underwater images. The pictures were pre-processed with the use of specific techniques, such as enhancement processing, Wiener filtering, median filtering and thresholding. Coral reef pictures were used as a dataset of underwater images to test the efficiency of each edge detection method used in the experiment. All coral reef image datasets were captured using an underwater GoPro camera. The performance of each edge detection technique was evaluated using mean square error (MSE) and peak signal to noise ratio (PSNR). The lowest MSE value and the highest PSNR value represent the best quality of underwater images. The results of the experiment showed that the Canny edge detection technique outperformed other approaches used in the course of the project.
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[1] M. Fatan, M. Daliri, and A. Shahri, „Underwater cable detection in the images using edge classification based on texture information", Measurement, vol. 91, pp. 309-317, 2017. DOI: https://doi.org/10.1016/j.measurement.2016.05.030
View in Google Scholar
[2] A. Saini and M. Biswas, „Object detection in underwater image by detecting edges using adaptive thresholding", in Proc. of 3rd Int. Conf. on Trends in Electron. and Informat. ICOEI 2019, Tirunelveli, India, 2019, pp. 628-63 2. DOI: https://doi.org/10.1109/ICOEI.2019.8862794
View in Google Scholar
[3] P. Princess, S. Silas, and E. B. Rajsingh, „Performance analysis of edge detection algorithms for object detection in accident images", in Proc. of Global Conf. for Advan. in Technol. GCAT 2019, Bangalore, India, 2019. DOI: https://doi.org/10.1109/GCAT47503.2019.8978438
View in Google Scholar
[4] K. Srividhya and M. Ramya, „Performance analysis of preprocessing filters for underwater images", in Proc. of Int. Conf. On Robot., Autom., Contr. and Embed. Syst. RACE 2015, Chennai, India, 2015. DOI: https://doi.org/10.1109/RACE.2015.7097234
View in Google Scholar
[5] M. A. Malborg, L. L. Lacatan, R. M. Dellosa, Y. D. Austria, and C. F. Cunanan, „Edge detection comparison of hybrid feature extraction for combustible fire segmentation: A Canny vs Sobel performance analysis", in Proc. of 11th IEEE Contr. and Syst. Graduate Res. Colloq. ICSGRC 2020, Shah Alam, Malaysia, pp. 318-322. DOI: https://doi.org/10.1109/ICSGRC49013.2020.9232632
View in Google Scholar
[6] R. Ramnarayan, N. Saklani, and V. Verma, „A review on edge detection technique canny edge detection", Int. J. of Comp. Appl., vol. 178, pp. 28-30, 2019. DOI: https://doi.org/10.5120/ijca2019918828
View in Google Scholar
[7] R. Song, Z. Zhang, and H. Liu, „Edge connection based Canny edge detection algorithm", Pattern Recog. and Image Anal., vol. 27, pp. 740-747, 2017. DOI: https://doi.org/10.1134/S1054661817040162
View in Google Scholar
[8] C. Jeong, H. Yang, and K. Moon, „A novel approach for detecting the horizon using a convolutional neural network and multiscale edge detection", Multidimens. Syst. and Sig. Proce., vol. 30, pp. 1187-1204, 2019. DOI: https://doi.org/10.1007/s11045-018-0602-4
View in Google Scholar
[9] Y. Zhang, X. Han, H. Zhang, and L. Zhao, „Edge detection algorithm of image fusion based on improved Sobel operator", in Proc. of IEEE 3rd Inform. Technol. and Mechatron. Engin. Conf. ITOEC 2017, Chongqing, China, 2017, pp. 457-461. DOI: https://doi.org/10.1109/ITOEC.2017.8122336
View in Google Scholar
[10] Y. Tian, L. Lan, and L. Sun, „A review of sonar image segmentation for underwater small targets", in Proc. of the 2020 Int. Conf. On Pattern Recogn. and Intell. Sys., Athens, Greece, 2020. DOI: https://doi.org/10.1145/3415048.3416098
View in Google Scholar
[11] M. Sudhakara and M. Meena, „An edge detection mechanism using L*A*B color-based contrast enhancement for underwater images", Indonesian J. of Elec. Engin. and Com. Sci., vol. 18, pp. 41-48, 2020. DOI: https://doi.org/10.11591/ijeecs.v18.i1.pp41-48
View in Google Scholar
[12] R. Priyadharsini, T. Sharmila, and V. Rajendran, „An efficient Edge detection technique using filtering and morphological operations for underwater acoustic images", in Proc. of the 2nd Int. Conf. on Inform. and Commun. Technol. for Competit. Strat., Udaipur, India, 2016. DOI: https://doi.org/10.1145/2905055.2905168
View in Google Scholar
[13] A. Bist and S. Sondhi, „Fractional order differentiator based filter for edge detection of low contrast underwater images", Int. J. of Electron., Elec. and Computat. Syst., vol. 6, no. 7, pp. 376-383, 2017.
View in Google Scholar
[14] H. A. Elsennary, M. E. Hussien, and A. E. Ali, „Edge detection of an image based on extended difference of Gaussian", Amer. J. of Comp. Sci. and Technol., vol. 2, no. 3, pp. 35-47, 2019. DOI: https://doi.org/10.11648/j.ajcst.20190203.11
View in Google Scholar
[15] S. Raj, C. Jose, and M. Supriya, „Hardware realization of Canny edge detection algorithm for underwater image segmentation using field programmable gate arrays", J. of Engin. Sci. and Technol., vol. 12, no. 9, pp. 2536-2550, 2017 [Online]. Available: http://jestec.taylors.edu.my/Vol%2012%20issue%209%20September%202017/12 9 19.pdf
View in Google Scholar
[16] M. Han, Z. Lyu, T. Qiu, and M. Xu, „A review on intelligence dehazing and color restoration for underwater images", IEEE Trans. on Syst., Man, and Cybernet.: Syst., vol. 50, no. 5, pp. 1820-1832, 2020. DOI: https://doi.org/10.1109/TSMC.2017.2788902
View in Google Scholar
[17] T. Liu, L. Wan, and X. Liang, „An image segmentation method of underwater targets based on active contour model", Appl. Mechan. and Mate., vol. 511-512, pp. 457-461, 2014. DOI: https://doi.org/10.4028/www.scientific.net/AMM.511-512.457
View in Google Scholar
[18] M. Gandhi, J. Kamdar, and M. Shah, „Preprocessing of nonsymmetrical images for edge detection", Augmented Human Research, vol. 5, Article no. 10, pp. 1-10, 2020. DOI: https://doi.org/10.1007/s41133-019-0030-5
View in Google Scholar
[19] A. Baareh, A. Al-Jarrah, A. M. Smadi, and G. Shakah, „Performance evaluation of edge detection using Sobel, Homogeneity and Prewitt algorithms", J. of Softw. Engin. and Appl., vol. 11, pp. 537-551, 2018. DOI: https://doi.org/10.4236/jsea.2018.1111032
View in Google Scholar
[20] B. Dhruv, N. Mittal, and M. Modi, „Comparative analysis of Edge detection techniques for medical images of different body parts", in Data Science and Analytics, 4th International Conference on Recent Developments in Science, Engineering and Technology, RED-SET 2017, Gurgaon, India, October 13-14, 2017, Revised Selected Papers, B. Panda, S. Sharma, and N. Roy, Eds. Communications in Computer and Information Science, vol. 799, pp. 164-176. Springer, 2018. DOI: https://doi.org/10.1007/978-981-10-8527-7_15
View in Google Scholar
[21] P. Ganesan and G. Sajiv, „A comprehensive study of edge detection for image processing applications", in Proc. Int. Conf. on Innovat. In Inform., Embedded and Commun. Syst. ICIIECS 2017, Coimbatore, India, 2017. DOI: https://doi.org/10.1109/ICIIECS.2017.8275968
View in Google Scholar
[22] R. Li, D. Han, J. Dezert, and Y. Yang, „A novel edge detector for color images based on MCDM with evidential reasoning", in Proc. 20th Int. Conf. on Inform. Fusion Fusion 2017, Xi'an, China, 2017. DOI: https://doi.org/10.23919/ICIF.2017.8009727
View in Google Scholar
[23] F. Bachofer, G. Quänähervä, T. Zwiener, M. Maerker, and V. Hochschild, „Comparative analysis of edge detection techniques for SAR images", Eur. J. of Remote Sens., vol. 49, pp. 205-224, 2016. DOI: https://doi.org/10.5721/EuJRS20164912
View in Google Scholar
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