Multi-target tracking method for seaborne rain and fog and jittering environment

By combining target augmentation segmentation and an improved Kalman filter algorithm with the YOLOv7Tiny-Lites network, the discontinuity and identity jump problems in multi-target tracking under marine rain, fog, and shaking environments were solved, achieving high recall and stable multi-target tracking performance.

CN121504990APending Publication Date: 2026-02-10DALIAN MARITIME UNIVERSITY
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Patent Information

Application Number
CN202511693494.0
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-18
Publication Date
2026-02-10

AI Technical Summary

Technical Problem

In rainy, foggy, and turbulent environments at sea, multi-target tracking is prone to problems such as discontinuous recognition trajectories, identity ID jumps, and high latency. Existing technologies lack systematic solutions, and stable tracking is particularly difficult to achieve under occluded and dense target conditions.

Method used

We employ a target enhancement segmentation strategy, multi-scale feature extraction, and an improved Kalman filter algorithm. We use the YOLOv7Tiny-Lites network for target detection and tracking, and combine adaptive anchors and a lightweight backbone network to achieve efficient processing of multi-target image data.

Benefits of technology

It improves the recall rate of target detection and the stability of tracking, and significantly enhances the perception performance of unmanned ships under rain, fog and shaking conditions at sea.

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Abstract

The invention discloses a multi-target tracking method for an offshore rain, fog and jitter environment, and the method comprises the steps: obtaining multi-target image data in the offshore rain, fog and jitter environment, and carrying out the target cutting and fusion of the multi-target image data based on a target enhancement segmentation strategy, and obtaining an enhanced sample image; mapping the enhanced sample image and target features in the multi-target image data into a candidate frame set of a pixel scale, and obtaining a sample data set containing adaptive Anchors according to the candidate frame set based on a clustering algorithm; performing model training on the multi-target detection network through the sample data set to obtain an optimal detection model so as to realize target detection; and defining a target state vector and a target observation vector according to a detection result, and realizing multi-target tracking under the marine rain and fog and jitter environment based on an improved Kalman filtering algorithm. The problems that in the prior art, the precision of multi-target detection under the marine rain and fog and jittering environment is insufficient, and a systematic solution for multi-target tracking under the marine rain and fog environment and the jittering scene is lacked are solved.
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