Open AccessA Comparative Study of Various Edge Detection Techniques for Underwater Images
Author(s) -
Ezmahamrul Afreen Awalludin,
Tengku Noorfarahana T. Arsad,
Wan Nural Jawahir Hj Wan Yussof,
Zainudin Bachok,
Muhammad Suzuri Hitam
Publication year - 2022
Publication title -
journal of telecommunications and information technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.151
H-Index - 12
eISSN - 1899-8852
pISSN - 1509-4553
DOI - 10.26636/jtit.2022.155921
Subject(s) - underwater , computer science , artificial intelligence , thresholding , computer vision , edge detection , padding , image processing , remote sensing , pattern recognition (psychology) , image (mathematics) , geology , oceanography , computer security
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.