LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection
arXiv:2607.08076v1 Announce Type: cross Abstract: The complementary information between RGB and IR images can significantly enhance object detection performance under extreme conditions. Existing methods prefer dual-stream CNN backbones built upon YOLO for feature extraction and focus on the design of feature fusion. In this paper, we introduce the Laplacian Decoupled Feature Enhancement block (LDFE) to fuse features from different stages of the dual-stream CNN backbone. By design, LDFE simultaneously considers the characteristics of modalities and structures for feature fusion by employing gl
The continuous drive for more robust AI perception systems, especially under challenging environmental conditions, pushes the development of advanced multi-modal fusion techniques.
Improved object detection in extreme conditions directly enhances the reliability of autonomous systems, surveillance, and defence applications.
This new LDFE block offers a more refined approach to integrating RGB and IR data, potentially leading to more accurate and resilient object detection in dual-stream CNNs.
- · Defence contractors
- · Autonomous vehicle developers
- · Surveillance technology providers
- · Computer vision researchers
- · Developers relying solely on single-modality object detection in challenging env
Enhanced performance of imaging systems in fog, smoke, or low light conditions.
Accelerated adoption of multi-modal sensing in commercial and military applications needing high reliability.
Reduced false positives and improved decision-making in critical AI-driven systems operating in adverse environments.
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Read at arXiv cs.AI