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Related Experiment Video

Updated: Jan 13, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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Source-Free Domain-Adaptive Semi-Supervised Learning for Object Detection in CCTV Images.

Hyejin Shin1, Gye-Young Kim1

  • 1Department of AI·SW Convergence, Soongsil University, Seoul 06978, Republic of Korea.

Sensors (Basel, Switzerland)
|January 10, 2026
PubMed
Summary

This study introduces a novel source-free semi-supervised domain adaptation framework for object detection in CCTV systems. The method enhances detection accuracy by fusing pseudo-labels and using static adversarial regularization, improving performance under domain shifts.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Object detection in Closed-Circuit Television (CCTV) systems suffers from performance degradation due to domain gaps.
  • Privacy concerns and data regulations necessitate source-free learning approaches, limiting data reuse.
  • Existing methods struggle with domain adaptation and privacy constraints in real-world CCTV applications.

Purpose of the Study:

  • To develop a stable and effective source-free semi-supervised domain adaptation framework for CCTV object detection.
  • To address performance degradation caused by domain shifts and privacy limitations.
  • To improve the reliability and accuracy of object detection in challenging real-world scenarios.

Main Methods:

  • Proposed a Mean Teacher-based framework integrating pseudo-label fusion from weakly and strongly augmented views for robust pseudo-labels.
Keywords:
CCTVdomain adaptationobject detectionsemi-supervised learningsource-free

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Related Experiment Videos

Last Updated: Jan 13, 2026

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  • Implemented static adversarial regularization (SAR) with a frozen adversarial head for stable domain-invariance constraints.
  • Utilized a time-varying exponential weighting strategy to balance labeled and unlabeled target data during training.

    • Main Results:

    • Achieved an average improvement of 7.2% in mAP@0.5 over existing methods across four benchmark scenarios.
    • Demonstrated a 6.8% gain in a low-label setting using only 2% labeled target data.
    • Showcased an average improvement of 5.4% under challenging domain shifts, including clear-to-foggy adaptation and synthetic-to-real transfer.

    Conclusions:

    • The proposed source-free semi-supervised domain adaptation framework is effective and stable for CCTV object detection.
    • The method significantly enhances object detection performance under domain shifts while respecting privacy constraints.
    • This approach offers practical relevance for real-world CCTV systems facing domain variability and data privacy regulations.