Ship feature re-identification method, application method and system based on deep learning

Abstract

The invention discloses a ship feature re-identification method, application method and system based on deep learning, and According to the method, a deep feature extraction network with the efficientprocessing capability in the field of image perception is used to extract the deep features with the distinction degree, the deep features with the high-level perception semantics is autonomously extracted, the change of pixel levels is not depended on, and the overall features are considered, so that the problems of overwater supervision, low ship information query efficiency and the like causedby inaccurate matching and high detection error rate are solved, and the target ship discrimination can be effectively carried out. The PCB partitioning and the matrix type operation matching have the good recognition effect for the ships only parts of which occur, the matching speed is high, the problems of ship transformation, hidden identity escape supervision and the like are solved, and theintelligent auxiliary effects on the maritime traffic management, accident investigation, water conservancy attack illegal sand mining, navigation channel ship gate passing charging, customs attack private activities and the like are achieved. Compared with a ship identification method in the prior art, the ship re-identification method has obvious advantages in efficiency, cost and accuracy.

Description

technical field [0001] The invention relates to the field of computer vision and image recognition, in particular to a deep learning-based ship feature re-recognition method, application method and system. Background technique [0002] At present, water traffic mainly includes the following characteristics: 1. The background of the water surface is volatile and is very sensitive to light; 2. The river surface does not have a delineated driving line like the road surface, and the range of angles for ships moving on the river surface is much larger than that of vehicles; 3. The river surface The ship driving scene is vast, and the size of the ship varies greatly, which can even reach more than 10 times; therefore, it is difficult to detect the scale of the ship target, and the application of intelligent video image analysis in the supervision of ships on the water is limited. [0003] In the prior art, the AIS system and the radar system are generally used in maritime supervis...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the above problems in the prior art, that is, in order to solve the problems of water supervision and low efficiency of ship information query due to inaccurate matching and high detection error rate, this application provides a ship feature re-identification method based on deep learning , application method and system

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