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.
Highlights
With the development of technology and the continuous growth of military power, countries around the world are shifting their military priorities to the ocean
Autonomous underwater vehicle (AUV) equipped with visual image acquisition equipment is often used for real-time detection in the underwater environment, which has made autonomous underwater robots widely used in military fields, for example, mine detection, intelligence collection, and offshore defense
Based on a cycle-consistent adversarial network and a conditional generation adversarial network, Li et al.[31] proposed a trainable end-to-end system of an underwater multistyle generation adversarial network to solve the problem of fewer underwater image dataset
Summary
With the development of technology and the continuous growth of military power, countries around the world are shifting their military priorities to the ocean. For the purpose of reducing the impact of different environments on target recognition, Parma University used multiple datasets to study the potential of vision-based target detection algorithms in underwater scenes.[25] Through the training of multiple datasets, the algorithm can accurately recognize targets in different underwater environments and provide new ideas for subsequent research on multidata information fusion. According to the experimental results, the proposed algorithm can accurately detect the straight lines that existed on manmade objects in complex underwater background It has excellent real-time performance, that is, only 17.22 ms per image of the best result. Due to the diverse shapes of underwater artificial devices in the real environment, it is hard to collect target images and train a satisfactory model These factors lead to low target recognition accuracy in the real environment.
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