Image recognition and understanding method for robotic vision tasks

By establishing a differential hedging process based on environmental benchmarks and local manifold measures, the semantic drift problem of robot vision systems in dynamic environments is solved, achieving stability and accuracy in target recognition and understanding under extreme conditions.

CN122135327AActive Publication Date: 2026-06-02伽利略(天津)技术有限公司
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
伽利略(天津)技术有限公司
Filing Date
2026-04-30
Publication Date
2026-06-02

AI Technical Summary

Technical Problem

In unstructured dynamic operating environments, existing robot vision systems struggle to effectively handle semantic perception biases caused by drastic changes in ambient light fields and interference from local shadows, leading to target loss or misidentification. Existing methods increase computational load and cannot effectively solve the problem of feature manifold entanglement.

Method used

By acquiring the original image sequence, establishing an environmental baseline, calculating the migration trajectory and semantic momentum features of the global anchoring quantity, performing feedforward correction, and utilizing local manifold measure and differential hedging processing, nonlinearly calibrating the gateway, the target boundary semantics of high curvature regions are preserved, thus achieving hedging processing.

Benefits of technology

It maintains the stability of semantic perception under extreme conditions, avoids flickering recognition confidence, improves the ability to capture subtle edge differences between the target and the background, and enhances the certainty and stability of recognition and understanding.

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Abstract

This invention relates to the field of image recognition technology and discloses an image recognition and understanding method for robot vision operations, comprising: acquiring original image sequences and establishing an environmental benchmark characterizing the initial state of the image background field; extracting feature distribution maps of adjacent image frames; calculating the global anchoring vector migration trajectory and extracting semantic momentum features characterizing the temporal evolution of the background field, and correcting the environmental benchmark accordingly; calculating the local manifold measure of local feature points relative to the corrected environmental benchmark to determine the topological strength; determining the differential hedging damping coefficient based on the topological strength and performing nonlinear hedging processing on the feature distribution map to filter out background field features and retain the semantics of target boundaries in high curvature regions; and semantically mapping the processed feature distribution map. This invention eliminates the phase offset caused by sampling delay through spatiotemporal feature collaborative correction, effectively protecting the topological structure of the feature manifold and solving the problem of target boundary recognition failure under complex working conditions.
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