Sonar Image Target Detection Based on Deep Learning

Abstract

The most popular and crucial piece of equipment for underwater target detection right now is side-scan sonar, which primarily plays a crucial role in underwater operations. Unfortunately, in general, side-scan sonar images have low resolution and suffer from significant noise interference, making target recognition with these images highly challenging. In this paper, we employ deep learning techniques to recognize underwater targets using side scan sonar imagery. This paper begins by outlining the current state of underwater target detection research as well as briefly describing its importance and historical context. The imaging process of side-scan sonar pictures is studied in greater depth, and it is determined that noise on sonar images is caused principally by the combined effect of external and internal noise. This causes extra noise in sonar pictures, lowering image quality and affecting target identification performance. Proper sonar image pre-processing findings can provide more consistent target detection help. After then, the YOLOv5 target detection algorithm is investigated and improved. By performing several sets of comparison trials, the updated technique improves the accuracy and speed of side-scan sonar image target recognition and finally overcomes the issues of missed detection, false detection, and low accuracy of overlapping tiny object recognition.