LDFE: Laplacian Decoupled Feature Enhancement Block for Dual-Stream CNN-based RGB-IR Object Detection

2026-07-09 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The Laplacian Decoupled Feature Enhancement (LDFE) block is introduced to significantly improve dual-stream CNN-based RGB-IR object detection, particularly under extreme conditions. Designed to fuse features from various stages of the CNN backbone, LDFE employs a sequential process of global-local decomposition, denoising, fusion, and reconstruction. It first separates features into global and local components using a Laplacian Pyramid. Denoising and fusion are then performed by the Global State Space Enhancement module (GS2E) and Local Convolutional Correlation Enhancement module (LC2E). GS2E utilizes a two-branch architecture with cross-modal attention for noise suppression and a State Space Model for long-range dependencies, dynamically alternating main/auxiliary modalities. LC2E focuses on local features, suppressing noise and extracting fine-grained details via triple convolution. This approach achieves mAP improvements over SOTA methods by 6.2% on M3FD, 3.7% on DroneVehicle, 4.7% on LLVIP, 2.3% on FLIR-Aligned, 4.1% on KAIST, and 2.0% on VEDAI datasets.

Key takeaway

For Computer Vision Engineers developing robust object detection in challenging environments, integrating the LDFE block into your dual-stream RGB-IR CNNs offers a significant performance uplift. You should consider its global-local feature decomposition and specialized denoising modules (GS2E, LC2E) to improve mAP by up to 6.2% on diverse datasets. This method provides a clear path to enhance detection accuracy where traditional single-modality or simpler fusion techniques fall short.

Key insights

The LDFE block enhances RGB-IR object detection by decoupling features into global and local components for specialized fusion and denoising.

Principles

• Decompose features into global and local parts.
• Dynamic cross-modal attention suppresses noise.
• State Space Models capture long-range dependencies.

Method

LDFE decomposes features via Laplacian Pyramid, then applies GS2E for global feature denoising and long-range dependency capture, and LC2E for local feature noise suppression and fine-grained detail extraction, followed by reconstruction.

In practice

• Integrate LDFE into YOLO-based dual-stream CNNs.
• Apply global-local decomposition for multimodal fusion.
• Utilize cross-modal attention for noise reduction.

Topics

• RGB-IR Object Detection
• Feature Enhancement
• Dual-Stream CNNs
• Laplacian Pyramid
• State Space Models
• Cross-Modal Attention
• YOLO Backbone

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.