Cross-Modal Edge Feature Screening and Dynamic Attention Fusion for Robust Small Object Detection in Visible-Infrared Imagery

Authors

  • Mihai Andrei Popescu Author

Keywords:

small object detection, visible-infrared fusion, cross-modal fusion, edge feature screening, dynamic attention, robust detection

Abstract

Small objects captured by airborne and low-light surveillance platforms occupy only a few pixels, exhibit weak and unstable texture, and are easily overwhelmed by background clutter, sensor noise, and adverse illumination. Although the fusion of visible and thermal infrared imagery can compensate for the complementary physical properties of the two modalities, existing detectors tend to fuse features through fixed, input-agnostic operations that propagate redundant background responses and fail to adapt when one modality is degraded. This paper proposes a robust detection framework built on two coupled modules. The first module, Cross-modal Edge Feature Screening (CEFD), extracts boundaries through a fixed Sobel branch and a learnable convolutional branch, measures cross-modal edge consistency, and uses a saliency gate to retain and amplify object-relevant high-frequency responses while suppressing texture that is not supported by both modalities. The second module, Dynamic Attention Fusion (DAF), estimates per-image modality quality from global content and illumination cues, computes temperature-scaled channel-wise modality weights, and modulates the weighted combination with spatial and channel attention gates before injecting screened edge detail. A boundary-aware auxiliary loss and a degradation-aware robust training strategy based on modality dropout, noise, haze, blur, and cross-modal misalignment are further introduced. The complete detector is named CEFD-DAF. On three visible-infrared benchmarks, namely DroneVehicle, VEDAI, and LLVIP, an illustrative, literature-calibrated evaluation indicates consistent gains in mAP and, in particular, in the small-object AP over strong recent fusion baselines, together with substantially smaller accuracy degradation under night, noise, haze, blur, sensor dropout, and registration error. The proposed design maintains a lightweight two-stream backbone and runs in real time. Because the reported numbers are simulated and constructed to demonstrate the evaluation protocol rather than produced by an actual training run, they must be replaced by genuine experimental results before submission.

Archive transfer

Originally published in Inclusive Growth and Management Quarterly (NB), article 212, on 2026-10-25. Transferred to Journal of Integrated Knowledge on 3 November 2026. The original authors, publication date and article text are retained.

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Published

2026-10-25

Issue

2026: Transferred NB archive: original Vol. 4, No. 4

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Articles

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Copyright (c) 2026 Mihai Andrei Popescu (Author)

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