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Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection.

Wentao Wu, Chenglong Li, Xiao Wang

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|June 16, 2026

Summary

This summary is machine-generated.

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Vector Functions and Motion: Problem Solving

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...

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This study introduces LPANet, a novel Large Language Model (LLM)-guided network for multimodal Unmanned Aerial Vehicle (UAV) object detection. LPANet enhances feature alignment to improve detection accuracy in challenging conditions.

Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Multimodal Unmanned Aerial Vehicle (UAV) object detection faces challenges due to semantic gaps between different data modalities.
Existing methods struggle with accurate semantic and spatial alignment, limiting overall detection performance.

Purpose of the Study:

To propose a novel network, LPANet (Large Language Model-guided Progressive feature Alignment Network), for enhanced multimodal UAV object detection.
To leverage Large Language Model (LLM) semantic features for progressive alignment between modalities.

Main Methods:

Utilized ChatGPT for fine-grained text descriptions and MPNet for semantic feature extraction from LLMs.
Developed a Semantic Alignment Module (SAM) to reduce inter-modal semantic differences.

Related Experiment Videos

Introduced Explicit Spatial Alignment Module (ESM) and Implicit Spatial alignment Module (ISM) for progressive spatial alignment.

Main Results:

LPANet demonstrated superior performance compared to state-of-the-art methods on public multimodal UAV datasets.
The proposed alignment strategy effectively addressed semantic and spatial misalignment issues.
LLM-guided semantic features provided crucial priors for cross-modal alignment.

Conclusions:

LPANet offers a significant advancement in multimodal UAV object detection by effectively bridging semantic gaps.
The progressive alignment strategy guided by LLM semantic features is key to improved detection accuracy.
The approach holds promise for various applications requiring robust object detection from diverse sensor data.