Ship intelligent identification tracking method

Abstract

The invention provides a ship intelligent identification tracking method. For the ship intelligent identification tracking method, based on a deep learning algorithm based on computer vision, a basicclassification network structure and a multi-scale target prediction method in traditional deep learning are improved, and a Darknet network and a YOLOv3 algorithm are combined to track a ship and detect and identify the type of the ship in real time. According to the ship intelligent identification tracking method, the idea of a residual error network is introduced; a full convolution structure is adopted; the network depth is increased; the data feature learning capability is improved; and local feature interaction between the feature maps is achieved in a convolution kernel mode through a YOLOv3 algorithm, and matching and positioning of targets are conducted, and target area prediction and category prediction are integrated into a single neural network model on the basis, so that global information of the images is recognized as the targets. Experimental results show that compared with a traditional method, the algorithm provided by the invention not only has better real-time performance and accuracy, but also has better robustness for various environmental changes.

Description

[0001] Technical field: [0002] The invention relates to the field of tracking and detection of surface ship targets, in particular to a method for intelligent identification and tracking of ships. [0003] Background technique: [0004] Ship tracking and recognition is a basic task of visual perception of intelligent ships. The current traditional tracking method is to track ships through the combination of AIS system and radar technology. For intelligent ships, the target ship cannot be regarded as a particle . Therefore, combining the advantages of neural network and Kalman filter algorithm, a ship dynamic tracking model is constructed. By modeling the background pixels of the maritime surveillance ship video, constructing a feature constraint equation to verify the motion state of the feature pixels, estimating the ship motion parameters to obtain the ship motion position parameters, and finally combining the active contour model algorithm to find the ship contour to dete...

AI Technical Summary

Problems solved by technology

Considering the change of ship imaging size in water traffic, the change of illumination, the change of ship imaging angle of view, the overlap of ship imaging in the cross-meeting situation, and human participation, etc., seriously affect the ship tracking recognition rate.

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