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SSRT-DETR: Domain-Adaptive Semi-Supervised Detector.

Wenshuai Zhang1, Dong Zhou1, Wenjie Xie1

  • 1The Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China.

Sensors (Basel, Switzerland)
|March 14, 2026
PubMed
Summary

This study introduces SSRT-DETR, a novel semi-supervised, domain-adaptive object detection framework. It enhances performance on challenging datasets by improving matching and pseudo-labeling strategies for domain shift.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Domain-adaptive object detection is crucial for real-world applications but faces challenges with domain shift.
  • Existing methods struggle with Hungarian matching sensitivity and fixed pseudo-label thresholds, especially with class imbalance and scene variability.

Purpose of the Study:

  • To develop a robust semi-supervised, domain-adaptive object detection framework (SSRT-DETR) overcoming limitations of current approaches.
  • To enhance object detection performance under domain shift by improving matching stability and adaptive pseudo-labeling.

Main Methods:

  • Utilized a mean teacher-student architecture with style-transferred images for joint domain modeling.
  • Introduced Domain-Aware Matching (DAM) to stabilize early cross-domain training by augmenting Hungarian matching.
Keywords:
RT-DETRdomain-adaptive object detectionsemi-supervised learning

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  • Developed Class-/Scene-Adaptive Pseudo-Labeling (CAP) to dynamically adjust thresholds based on class and scene characteristics.

    • Main Results:

    • SSRT-DETR significantly improved detection performance on benchmarks like Cityscapes→Foggy Cityscapes (mAP@0.5 from 51.0 to 54.3).
    • Achieved state-of-the-art results on KITTI→Cityscapes and Sim10K→Cityscapes for car detection (67.3 AP and 64.9 AP).
    • Demonstrated consistent gains in rare categories and adverse weather, validating DAM and CAP effectiveness while maintaining real-time efficiency.

    Conclusions:

    • The proposed SSRT-DETR framework effectively addresses domain-adaptive object detection challenges.
    • DAM and CAP modules are key innovations enabling robust performance across diverse and challenging scenarios.
    • SSRT-DETR offers a promising solution for real-time, domain-adaptive object detection.