Noted: The paper is under review, and this code repository will be updated after it is accepted

RSDet

Removal and Selection: Improving RGB Infrared Object Detection via Coarse to Fine Fusion

Paper: (arXiv 2401.10731)

Based on the MMdetection 3.1.0 framework, this project modifies its data flow and related classes and functions, and changes the MMdetection to a multi-modal detection framework to facilitate RGBT Object Detection.

Overview

Main Results

Dataset: FLIR, LLVIP and Kaist

Getting Started

Installation

ref : mmdetection installation

Step 1: Clone the RSDet repository:

To get started, first clone the RSDet repository and navigate to the project directory:

git clone https://github.com/Zhao-Tian-yi/RSDet.git
cd RSDet

Step 2: Environment Setup:

RSDet recommends setting up a conda environment and installing dependencies via pip. Use the following commands to set up your environment:

Create and activate a new conda environment

conda create -n RSDet
conda activate RSDet

If you develop and run mmdet directly, install it from source

pip install -v -e .

Install Dependencies

pip install -r requirements.txt
pip install -r requirements_rgbt.txt

Result

Kaist Result

Citation

@article{zhao2024removal,
  title={Removal and Selection: Improving RGB-Infrared Object Detection via Coarse-to-Fine Fusion},
  author={Zhao, Tianyi and Yuan, Maoxun and Wei, Xingxing},
  journal={arXiv preprint arXiv:2401.10731},
  year={2024}
}

✅ Updates

• March. 19th, 2024: Update: we have updated the source about one version bug.

• March. 12th, 2024: The source code is provided.

Acknowledgment