Underwater Target Detection Based on Sonar Image

Abstract

Underwater target detection by processing sonar images is of great military and civil significance. This paper comprehensively describes the principles, methods, algorithms, and development trends in underwater target detection based on sonar images. Initially, we divide the underwater target detection task based on sonar images into traditional, deep learning-based, and combined deep learning- and transfer learning-based underwater target detection. Traditional target detection is divided into underwater target detection based on mathematical statistics, mathematical morphology, and pixels. Deep learning-based target detection methods are primarily divided into one-stage, two-stage, and detection transformer(DETR) methods. Combined deep learning- and transfer learning-based target detection is primarily divided into target detection based on simple deep neural network model transfer and complex deep learning model transfer. Finally, the advantages and disadvantages of the existing technology are summarized, and the future development direction of this field is discussed.