A Comparative Study of Various Edge Detection Techniques for Underwater Images

Abstract

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.

Performance Comparison of Edge Detection Method for Extracting Images of Lutjanus spp. Contour

“…Then it can be concluded that Canny's algorithm has the best performance with entropy and PSNR on Tisha images 1.5701 and 10.9043 and Diya images 1.5477 and 9.6982. [26] study used the Sobel, Prewitt, Roberts, Laplacian, and Canny algorithms to detect Acropora branches underwater. In this study, 100 Acropora branching image data were used.…”