Water surface target detection tracking integration method facing unmanned ship application

Abstract

A water surface target detection tracking integration method facing a unmanned ship application is disclosed. The method comprises the following steps of S1, training a target detection nerve network model; S2 carrying out target detection and putting a detection result into a set Bd; S3, detecting a target tracking result, inputting the target tracking result of a previous frame of picture into the target detection nerve network model so as to carry out detection, retaining a tracking frame which does not lose a target, and putting the tracking frame into a set Bt; S4, adjusting sizes and positions of a target frame and the tracking frame in a fine mode; S5, determining whether there is a new target; S6, carrying out target tracking; and S7, continuously tracking m frames, and then repeating the step2 to step7 till the last frame of picture is tracked. In the invention, advantages of target detection and target tracking are combined, accuracy of the method is high and a speed is fast, and simultaneously stable target position information can be acquired; and effectively technology guarantee is provided for autonomous obstacle avoidance of an unmanned ship.

Description

technical field [0001] The invention belongs to the cross-technical field of digital image processing and control systems, and more specifically relates to an integrated method for surface target detection and tracking for unmanned boat applications. Background technique [0002] As an unmanned surface ship, unmanned ships play a very important role in many practical application scenarios. For example, water quality monitoring in the civilian field, customs anti-smuggling in the police field, offshore patrol in the military field, and so on. Therefore, my country's demand for unmanned boats is increasing day by day. The prerequisite for autonomous navigation of unmanned boats is to realize automatic obstacle avoidance technology, and the rapid detection of surface targets is the basis of automatic obstacle avoidance technology. In addition to fast and accurate detection of surface targets, the detection results must be smooth so as to provide stable environment perception ...

AI Technical Summary

Problems solved by technology

The target tracking algorithm is fast and the detection results are relatively stable, but the tracking algorithm also has many limitations

First, the target tracking algorithm needs to be provided with the initial position of the target. Due to the large number of surface targets, if a new target appears, it will not be able to track

Second, it is difficult for the target tracking algorithm to overcome large-scale changes. If the target has a large degree of attitude change, the target will be lost

Third, the target tracking algorithm cannot solve the occlusion problem. There are many ships on the surface, and occlusion often occurs. The target tracking algorithm is easy to lose the target.

[0010] To sum up, neither the target detection algorithm nor the target tracking algorithm can complete the obstacle detection and recognition task of the unmanned vehicle alone, and cannot realize the autonomous obstacle avoidance of the unmanned vehicle.

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