RoboFireFuseNet

This is the official repository for our recent work: RoboFireFuseNet: Robust Fusion of Visible and Infrared WildfireImaging for Real-Time Flame and Smoke Segmentation

Abstract

Concurrent segmentation of flames and smoke is challenging, as smoke frequently obscures fire in RGB imagery. Existing multimodal models are either too computationally demanding for real-time deployment or too lightweight to capture fine fire details that may escalate into large wildfires. Moreover, they are typically trained and validated on simplistic datasets, such as Corsican and FLAME1, which lack the dense smoke occlusion present in real-world scenarios. We introduce RoboFireFuseNet (RFFNet), a real-time deep neural network that fuses RGB and infrared (IR) data through attention-based mechanisms and a detail-preserving decoder. Beyond strong performance, RFFNet establishes a benchmark on a challenging, real-world wildfire dataset with dense smoke, creating a foundation for fair comparison in future flame-and-smoke segmentation research. Despite its lightweight design, it achieves state-of-the-art results on a general urban benchmark, demonstrating both efficiency and versatility. Its combination of accuracy, real-time performance, and multimodal fusion makes RFFNet well-suited for proactive, robust and accurate wildfire monitoring.

Highlights

• 🔄 Multimodal Fusion: Combines CNN and attention blocks to extract cross-modal, spatiotemporal features.
• ⚡ Real-Time Performance: Designed for lightweight, real-world wildfire detection with high efficiency.
• 🔥 Flame & Smoke Segmentation: Handles dense smoke coverage while detecting small flame spots.
• 🎯 Compact & Efficient: Achieves competitive segmentation accuracy with fewer parameters than state-of-the-art models.

Updates

• Paper is submitted to ...

Demo

Overview

Schematic overview of the proposed fusion model and

Fusion Architecture

Our model enhances PIDNet-Small by integrating SwinV2-T Transformer blocks, improving capacity and capturing long-range dependencies. To better preserve and extract modality-specific features, we introduce dedicated modality pathways. Additionally, we replace basic upscaling with a U-Net-style decoder, enhancing spatial reconstruction and producing high-resolution segmentation maps.

📊 Performance Comparison on FLAME2

Method	Avg Recall (%)	MIoU (%)	Params (M)
PIDNet-RGB	75.66	61.21	34.4
PIDNet-IR	83.05	58.71	34.4
PIDNet-Early	88.25	73.90	34.4
MFNet	93.53	80.26	0.73
EFSIT*	90.15	80.09	4.8
RTFNet	73.87	65.42	185.24
GMNet	67.53	54.08	153
EGFNet	74.27	60.98	62.5
CRM-T	-	-	59.1
Sigma-T	92.6	86.27	48.3
Ours	94.37	88.17	29.5

📊 Performance Comparison on Urban Scenes (MFNet)

Method	Avg Recall (%)	MIoU (%)	Params (M)
PIDNet-m RGB	65.59	51.52	34.4
PIDNet-m IR	65.27	50.70	34.4
PIDNet-m Early	69.59	52.62	34.4
MFNet	59.1	39.7	0.73
RTFNet	63.08	53.2	185.24
GMNet	74.1	57.3	153
EGFNet	72.7	54.8	62.5
CRM-T	-	59.7	59.1
Sigma-T	71.3	60.23	48.3
Ours	71.1	60.6	29.5

Usage

0. Setup

• Install python requirements: pip install -r requirements.txt or recommender Python 3.10.12
• Download the weights inside the weights folder.
• Download the preprocessed data inside the data folder. We include our annotations on public FLAME2 dataset.

2. Training

Customize configurations via the config/ folder or override them with inline arguments.

• train fusion model on wildfire: python train.py --yaml_file wildfire.yaml --LR 0.001 --BATCHSIZE 5 --WD 0.00005 --SESSIONAME "train_simple" --EPOCHS 500 --DEVICE "cuda:0" --STOPCOUNTER 30 --ONLINELOG False --PRETRAINED "weights/pretrained_480x640_w8_2_6.pth" --OPTIM "ADAM" --SCHED "COS"
• train fusion model on urban dataset: python train.py --yaml_file urban.yaml --LR 0.001 --BATCHSIZE 5 --WD 0.00001 --SESSIONAME "train_simple" --EPOCHS 500 --DEVICE "cuda:0" --STOPCOUNTER 30 --ONLINELOG False --PRETRAINED "weights/pretrained_480x640_w8_2_6.pth" --OPTIM "ADAM" --SCHED "COS"

3. Testing

Customize configurations via the config/ folder or override them with inline arguments.

• test fusion model on wildfire: python test.py --yaml_file wildfire.yaml --SESSIONAME "train_simple" --DEVICE "cuda:0" --PRETRAINED "weights/robo_fire_best.pth"
• test fusion model on urban dataset: python test.py --yaml_file urban.yaml --SESSIONAME "train_simple" --DEVICE "cuda:0" --PRETRAINED "weights/robo_urban.pth"
• To run the demo with custom images, place your files in the outputs/demo folder using the following naming conventions: <prefix>_rgb_<postfix>.png for RGB images, <prefix>_ir_<postfix>.png for IR images, and a .txt file with rows formatted as <prefix>_XXX_<postfix>.png. Replace <prefix> and <postfix> with any values, ensuring rgb and ir indicate the modality. Optionally, include ground truth files named <prefix>_gt_<postfix>.png to calculate metrics. Run the demo using python test.py --yaml_file wildfire_demo.yaml for the wildfire demo or python test.py --yaml_file urban_demo.yaml for the urban demo for urban one. If you use custom .txt file instead of demo_fire.txt and demo_urban.txt adjust the YAML config files.

Citation

(TODO: complete the information when paper accepted)

Acknowledgment

[1] : PIDnet: Xu, Jiacong et al. “PIDNet: A Real-time Semantic Segmentation Network Inspired by PID Controllers.” 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2022): 19529-19539.
[2] : FLAME2: Bryce Hopkins, Leo O'Neill, Fatemeh Afghah, Abolfazl Razi, Eric Rowell, Adam Watts, Peter Fule, Janice Coen, August 29, 2022, "FLAME 2: Fire detection and modeLing: Aerial Multi-spectral imagE dataset", IEEE Dataport, doi: https://dx.doi.org/10.21227/swyw-6j78.