Linfeng-Tang/SeAFusion

What it solves

SeAFusion addresses the challenge of merging infrared and visible images into a single composite image that is not only visually clear but also highly useful for high-level vision tasks like object detection and semantic segmentation. Traditional fusion methods often ignore the semantic meaning of the image, which can lead to results that are less effective for downstream AI analysis.

How it works

The project implements a semantic-aware, real-time network designed to fuse infrared and visible imagery. It utilizes a network architecture based on gradient residual dense blocks to process the images. By integrating the fusion process "in the loop" of high-level vision tasks, the network ensures that the resulting fused images preserve the critical semantic information needed for accurate detection and segmentation.

Who it’s for

This tool is for researchers and developers working in computer vision, specifically those focusing on multi-modal image fusion for surveillance, autonomous driving, or any application where combining thermal and visual data is essential for machine perception.

Highlights

• Semantic-Aware: Specifically designed to improve the performance of downstream tasks like object detection (using YOLOv5) and semantic segmentation (using Deeplabv3+).
• Real-Time Performance: Built for efficiency to allow for real-time processing of image streams.
• High-Level Vision Integration: Validated against standard benchmarks like the MFNet dataset to ensure the fused output is optimized for AI-driven analysis.