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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Cross-Modal Object Detection Based on Content-Guided Feature Fusion and Self-Calibration.

Liyang Ning1, Xuxun Liu1,2, Luoyu Zhou1

  • 1School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)
|September 19, 2025
PubMed
Summary

This study introduces a novel dual-backbone model for cross-modal object detection, enhancing feature representation and accuracy by integrating transformer and convolution operations. The proposed method significantly improves detection performance in diverse environments.

Keywords:
YOLOv8cross-modalobject detectionself-calibrationtransformer

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional transformers have limitations in local attention, hindering feature representation and accuracy in cross-modal object detection.
  • Deep features can degrade through convolutional layers, leading to loss of critical object details.

Purpose of the Study:

  • To develop an advanced dual-backbone cross-modal object detection model.
  • To overcome limitations in local attention and feature degradation in existing models.

Main Methods:

  • A parallel network was introduced in the backbone for simultaneous multi-modal processing.
  • A content-guided fusion (CGF) module combined transformer and convolution for global and local feature extraction.
  • An adaptive calibration fusion (ACF) module merged shallow and deep features to retain fine-grained details.

Main Results:

  • The model achieved mAP50 of 96.4 and mAP95 of 63.8 on the LLVIP dataset.
  • On the M3FD dataset, the model reached mAP50 of 83.7 and mAP95 of 56.6.
  • Outperformed baseline and state-of-the-art methods in detection accuracy.

Conclusions:

  • The proposed dual-backbone model effectively enhances cross-modal object detection.
  • The novel fusion modules improve feature representation and detection accuracy in complex scenarios.
  • Demonstrated robust performance across various environments for cross-modal object detection tasks.