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The Enhance-Fuse-Align Principle: A New Architectural Blueprint for Robust Object Detection, with Application to

Yuduo Lin1, Yanfeng Lin2, Heng Wu1,3

  • 1Guangdong Provincial Key Laboratory of Cyber-Physical System, School of Automation, Guangdong University of Technology, Guangzhou 510006, China.

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Summary

This study introduces the Enhance-Fuse-Align (E-F-A) principle for object detection in noisy images. The SecureDet model, using this principle, significantly improves detection accuracy in challenging domains like X-ray security screening.

Keywords:
enhance-fuse-align principlemulti-scale feature fusionobject detectionsecurity screeningsignal degradation

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Image Processing

Background:

  • Object detection in security, medical, and satellite imaging faces challenges from signal degradation (noise, blur) and spatial ambiguity (occlusion, scale variation).
  • Standard architectures often fuse multi-scale features prematurely, amplifying noise and hindering performance.

Purpose of the Study:

  • To introduce the Enhance-Fuse-Align (E-F-A) principle as a new architectural blueprint for robust object detection.
  • To develop and validate a model, SecureDet, implementing the E-F-A principle.

Main Methods:

  • The Enhance-Fuse-Align (E-F-A) principle involves sequential feature enhancement, weighted fusion, and contextual/spatial alignment.
  • SecureDet integrates RFCBAMConv for enhancement, BiFPN for fusion, and ECFA/ASFA modules for alignment.
  • The model was applied to X-ray contraband detection for validation.

Main Results:

  • SecureDet, based on the E-F-A principle, significantly outperformed baseline and improperly ordered architectures in X-ray contraband detection.
  • Ablation studies confirmed the critical importance of the E-F-A sequence for performance gains.
  • Enhancement before fusion attenuated noise, and alignment corrected mis-registrations, improving detection.

Conclusions:

  • The Enhance-Fuse-Align (E-F-A) principle is essential for effective object detection in degraded imaging domains.
  • The proposed SecureDet model demonstrates the practical efficacy of the E-F-A blueprint.
  • The findings highlight the necessity of ordered enhancement and alignment prior to feature fusion for improved robustness.