Multi-AUV underwater target identification method based on super-resolution selectable network

Abstract

The invention discloses a multi-AUV underwater target identification method based on a super-resolution selectable network, and the method comprises the steps: collecting acoustic image and optical image information, carrying out the color recovery and data reconstruction, improving the image quality through image super-resolution, and achieving the super-resolution of an underwater image; target feature extraction and target feature similarity measurement: based on a lightweight convolutional neural network, fusing the features of the target information collected by the multiple AUVs, and calculating the similarity between the features by adopting a mahalanobis distance; a threshold value is set, a learning model is designed according to the relation between the threshold value and the similarity, and target recognition under different conditions is carried out; and when the similarity is higher than a threshold value, the improved transfer learning is adopted for identification, so that the calculation energy consumption of the AUV is reduced, and the real-time performance of the algorithm is ensured. And when the similarity is lower than a threshold value, identifying the target by adopting few-sample learning, carrying out centralized training on target information with unobvious features caused by a complex background, extracting effective features, reducing interference of environmental factors, and realizing efficient underwater target identification of multiple AUVs (Autonomous Underwater Vehicles).

Description

Technical field [0001] The invention belongs to the technical field of seabed surveying, and specifically relates to a multi-AUV underwater target recognition method based on a super-resolution selectable network to realize AUV ocean target recognition. Background technique [0002] AUV can be widely used in scientific research investigation, military, civilian and other fields. The data collected by the acoustic and optical sensors mounted on it are increasingly used. Sensor information recognition plays an important role in the research field of ocean perception, such as obstacle detection to avoid risks, underwater structure safety inspection, and searching for targets of interest. In underwater imaging systems, acoustic sensors and optical sensors are the two main imaging modes used for underwater detection. Online recognition of acoustic images and optical images is the key to the autonomous capabilities of AUVs. [0003] Traditional underwater target recognition is m...

AI Technical Summary

Problems solved by technology

[0005] The present invention proposes a multi-AUV underwater target recognition method based on a super-resolution selectable network to solve the problems of low recognition rate caused by defects such as low underwater image resolution, lack of samples, and inability to effectively identify unknown targets.

Images