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An enhanced RT-DETR with frequency decoupling and orthogonal regularization for UAV infrared small target detection
1Jilin University of Chemical Technology, No. 45 Chengde Street, Tiedong Street, L Jilin 132022, Jilin Province, China.
Iscience
|August 13, 2026
Summary
This study introduces an enhanced real-time detection transformer for infrared small target detection in drone imagery. The novel approach improves accuracy and localization speed for low-light surveillance applications.
Area of Science:
- Computer Vision
- Artificial Intelligence
- Remote Sensing
Background:
- Infrared small target detection in unmanned aerial vehicle (UAV) imagery is crucial for low-light surveillance.
- Existing methods face challenges like feature redundancy, poor context modeling, and gradient vanishing in tiny target localization.
Purpose of the Study:
- To develop an enhanced real-time detection transformer specifically for drone-based infrared scenarios.
- To overcome limitations of current frameworks in detecting small targets under challenging conditions.
Main Methods:
- Proposed an enhanced real-time detection transformer incorporating an Ortho-Block module for representation purification.
- Introduced an AIFI-HiLo module for decoupling scene semantics and capturing dense targets.
- Utilized a dual-stream GLSA mechanism with deformable convolutions for scale variation adaptation and NWD-SIoU loss for gradient enhancement.
Main Results:
- Achieved a 4.8% mAP50 improvement over the RT-DETR baseline on the HIT-UAV dataset.
- Demonstrated robust cross-scene adaptability on the VisDrone2019 dataset.
- The proposed method enhances detection accuracy and localization convergence speed.
Conclusions:
- The enhanced real-time detection transformer effectively addresses bottlenecks in infrared small target detection for UAVs.
- The framework shows significant performance gains and adaptability across different datasets and scenarios.
- This work contributes a more robust and efficient solution for drone-based infrared surveillance.
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The LOD indicates the presence or absence...
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