Underwater target recognition methods based on the framework of deep learning: A survey. (15th December 2020)

• Record Type: Journal Article
• Authors: Teng, Bowen; Zhao, Hongjian
• Is Part Of: International journal of advanced robotic systems. Volume 17:Number 6(2020:Nov./Dec.)
• Publication Date: 2020-12-15
• Subjects: Deep learning -- AUV -- dangerous target recognition -- few-shot target recognition -- environmental interference; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881420976307
• Languages: English
• ISSNs: 1729-8806

Abstract

The accuracy of underwater target recognition by autonomous underwater vehicle (AUV) is a powerful guarantee for underwater detection, rescue, and security. Recently, deep learning has made significant improvements in digital image processing for target recognition and classification, which makes the underwater target recognition study becoming a hot research field. This article systematically describes the application of deep learning in underwater image analysis in the past few years and briefly expounds the basic principles of various underwater target recognition methods. Meanwhile, the applicable conditions, pros and cons of various methods are pointed out. The technical problems of AUV underwater dangerous target recognition methods are analyzed, and corresponding solutions are given. At the same time, we prospect the future development trend of AUV underwater target recognition.