Yunnan University

Edit Project

synthetic fire-smoke

Object Detection

Roboflow Universe Yunnan University synthetic fire-smoke
4

synthetic fire-smoke Computer Vision Project

Download this Dataset Try Pre-Trained Model
TRY THIS MODEL
Drop an image or

Images

3974 images
Explore Dataset

MSFFD

English | 中文简体

1. MSFFD Download Link

We provide two ways to access the MSFFD:

2. Scenario introduction

In order to achieve a realistic simulation of forest fires, we use Unreal Engine 5 to build a diverse forest scene.

Unreal Engine 5 can meet the details of real-world forest terrain, weather, time, lighting, texture, etc. It can also simulate multi-scale forest fires in different periods, which is ideal for the construction of synthetic datasets.

We built eight scenes with different terrain through Unreal Engine 5, containing three types of weather conditions: sunny, foggy, and rainy-snowy, and three different times of day, evening, and night.

The following table shows the details of the raw data.

Scenario Resolution FPS Duration Size Weather The number of objects Description
1 a 1480×684 17.6 126 seconds 270.7 Mb Sunny Multiple Objects Daylight, mountains, and plains
2 a 1480×684 17.7 85 seconds 227.5 Mb Sunny No Objects Evening, mountains, and plains
3 a 1480×684 17.2 109 seconds 30.2 Mb Sunny Multiple Objects Night, mountains, and plains
4 a 1480×684 13.8 79 seconds 184.1 Mb Sunny No Objects Evening, mountains, and plains
5 a 776×452 18.6 113 seconds 75.3 Mb Sunny No Objects Daylight, mountains, and plains
6 a 1480×684 17.9 82 seconds 9.7 Mb Sunny Single Object Night, mountains, and plains
7 b 1480×684 29.9 115 seconds 496.2 Mb Sunny Multiple Objects Daylight, mountains, and plains
8 b 1480×684 28 82 seconds 314.4 Mb Sunny No Objects Daylight, Evening, Night, mountains, and plains
9 b 1480×684 29.9 101 seconds 477.4 Mb Sunny Single Object Daylight, mountains, and plains
10 b 1480×684 26.5 83 seconds 43.4 Mb Sunny Single Object Night, mountains, and plains
11 c 1480×684 18.5 195 seconds 258.4 Mb Sunny Multiple Objects Daylight, Night, mountains, and plains
12 c 1480×684 21.2 190 seconds 423.3 Mb Sunny Single Object Daylight, Evening, Night, mountains, and plains
13 d 1480×684 9.99 133 seconds 145.9 Mb Sunny Single Object Daylight, Evening, Night, mountains, lakes, and plains
14 d 1480×684 15.7 122 seconds 169.2 Mb Sunny Multiple Objects Daylight, Evening, Night, mountains, lakes, and plains
15 d 1480×684 11.1 125 seconds 136.5 Mb Sunny No Objects Daylight, Evening, Night, mountains, lakes, and plains
16 e 1280×720 30 148 seconds 348 Mb Sunny Multiple Objects Daylight, mountains, and lakes
17 e 1280×720 30 97 secnds 130.2 Mb Sunny Multiple Objects Night, mountains, and lakes
18 e 1280×720 30 89 seconds 92 Mb Sunny Multiple Objects Evening, mountains, and lakes
19 f 1280×720 24.6 60 seconds 342.8 Mb Rainy&Fog Multiple Objects Daylight, mountains, lakes, and plains
20 f 1280×720 23.7 29 seconds 20.2 Mb Rainy&Fog Multiple Objects Evening, Night, mountains, lakes, and plains
21 f 1280×720 10.6 57 seconds 6.7 Mb Rainy&Fog Multiple Objects Night, mountains, lakes, and plains
22 f 1280×721 10.6 36 seconds 28.1 Mb Rainy&Fog Multiple Objects Evening, mountains, lakes, and plains
23 g 1280×722 10.2 99 seconds 206 Mb Sunny Multiple Objects Daylight, plains
24 g 1280×723 10.5 27 seconds 21.8 Mb Sunny Multiple Objects Night, plains
25 g 1280×724 9.3 61 seconds 92 Mb Sunny Multiple Objects Evening, plains
26 h 1280×724 24.7 40 seconds 251.1 Mb Snow Multiple Objects Daylight, mountains, and plains
27 h 1280×724 26 33 seconds 143.4 Mb Snow Multiple Objects Night, mountains, and plains
28 h 1280×724 25.3 49 seconds 183.6 Mb Snow Multiple Objects Evening, mountains, and plains

2.1 Diverse scenarios

We built eight multi-scale forest scenarios with different terrain and vegetation.

8个不同地形的森林场景，包含平原、山脉、湖泊和河流。

2.2 Multiple Weather

MSFFD simulates not only sunny weather scenes, but also rainy, foggy and snowy weather scenes.

多样化气象条件，包含晴天、雨&雾天和雪天。

2.3 Multiple times of day

MSFFD considers multimodal forest fire images at different points of the day (day, evening and night).

image

2.4 Different number of fire objects

In a multimodal forest scenario with multiple terrains, multiple meteorological conditions, and multiple time points, we set up three fire target number scenarios: no objects, single object, and multiple objects, aiming to fully evaluate the performance of the simulated multimodal forest fire dataset in terms of target detection algorithm performance.

image

3. Details of the dataset

The following table shows the distribution of the images and annotated boxes of the dataset in different weather and at different times of the day.

Multimodal scenarios Number of images fire smoke Number of annotation boxes Average number of instances boxes per image
Sunny 3292 8423 7095 15518 4.7
Snowy 312 772 547 1319 4.2
Rainy & Fog 370 432 494 926 2.5
Daytime 2209 4684 3807 8491 3.8
Evening 560 2052 1581 3633 6.5
Night 1205 2891 2748 5639 4.7

The following table shows the size distribution of all comment boxes.

class small boxes medium boxes large boxes total
fire 4848 4114 665 9627
smoke 1883 2037 4216 8136
all 6731 6151 4881 17763

The following figure shows the distribution of all annotation boxes in the dataset, as shown below. image

The distribution of the centroid coordinates of the fire and smoke annotation boxes in different modes is shown in the following figure. image

The size distribution of fire and smoke annotation boxes for different modes is shown in the following figure. image

4. Experimental results

4.1 Experimental results of MSFFD in single-stage object detection algorithm

Model #Param. Flops Weight Size AP50:95 AP50 APFire APSmoke Precision Recall F1-socre
YOLOv8m 25.9M 78.9G 52Mb 51.2 87.3 88.7 86 83.7 86.9 85.3
YOLOv8l 43.7M 165.2G 87.7Mb 51.6 86.8 88.9 84.8 84.6 80.1 82.3
YOLOv8x 68.2M 258.1G 136.7Mb 47.3 87.1 88.5 85.8 84.4 80.2 82.2
YOLOv7 36.5M 103.2G 74.8Mb 48.5 87.5 88.7 86.2 85.3 80.5 82.8
YOLOv7-X 70.8M 188G 142.1Mb 47.3 87.1 88.5 85.8 84.4 80.2 82.2
YOLOv6-M 34.9M 85.8G 74.9Mb 44.5 83.4 84.2 82.6 83.8 80.7 82.2
YOLOv6-L 59.5M 150.7G 117.6Mb 48.8 86.3 87.2 85.4 84.2 79.8 81.9
YOLOv6-M6 79.6M 379.5G 72.51Mb 43.7 82.9 82 85.1 82.6 80 81.3
YOLOv6-L6 140.4M 673.4G 268.4Mb 46.3 83.3 83.9 82.6 83.6 79.7 81.6
YOLOv5m 21.2M 49.0G 42.2Mb 45.6 87.1 88.1 86 86.6 81.5 84.0
YOLOv5l 46.5M 109.1G 92.8Mb 45.9 86.7 87.9 85.4 85.2 81.6 83.4
YOLOv5x 86.7M 205.7G 173.1Mb 45.7 86.3 87.3 85.3 84.6 82.3 83.4
YOLOv4 - 59.6G 256Mb - 75.3 75.7 74.8 66 73 69.3
YOLOv4-CSP - 50.3G 210.3Mb - 81.5 83.8 79.3 78 80 79.0
YOLOv3 - 65.3G 246.3Mb - 82.3 82.6 82 83 77 79.9
YOLOv3-spp - 65.7G 250.5Mb - 82.1 82.3 81.8 83 78 80.4

4.2 Experimental results in two-stage object detection algorithm

Model Image Size Weight Size AP50:95 AP50
Faster R-CNN R50-DC5 640 1300Mb 38.6 78.9
Faster R-CNN R50-FPN 640 333.3Mb 42.1 82.7
Faster R-CNN R50-C4 416 257.5Mb 24.8 59.8
Faster R-CNN R101-C4 416 422Mb 26.9 60
Faster R-CNN R101-DC5 416 1400Mb 25.3 57.8
Faster R-CNN R101-FPN 416 365.2Mb 35.9 76.1
RPN R50 416 103.5Mb 25.4 55.8
Fast R-CNN R50-FPN 416 318.7Mb 28.5 59.8

4.3 Experimental Results of MSFFD in Lightweight objects detection algorithm

Model #Param. Flops Weight Size AP50:95 AP50 APFire APSmoke Precision Recall F1-socre
YOLOv8n 3.2M 8.7G 6.3Mb 49.5 86.4 87.1 85.7 83.2 81.2 82.2
YOLOv8s 11.2M 28.6G 22.5Mb 50.3 86.4 87.9 85 83.1 81.4 82.2
YOLOv6-N 4.7M 11.4G 10.0Mb 42 82.2 82.9 81.5 83.3 78.8 81.0
YOLOv6-S 18.5M 45.3G 10.2Mb 44.4 84.1 85 83.2 83.9 80.3 82.1
YOLOv6-N6 10.4M 49.8G 21.83Mb 42.1 82.1 82.8 81.3 83.7 78.9 81.2
YOLOv6-S6 41.4M 198G 85.84Mb 43.2 83.1 83.8 82.5 83.6 79.4 81.4
YOLOv5n 1.9M 4.5G 3.8Mb 40.1 84.9 86.1 83.7 84.3 79.1 81.6
YOLOv5s 7.2M 16.5G 14.4Mb 43.7 86.4 87.9 84.9 83.7 80.9 82.3
YOLOv4-tiny - 6.8G 23.5Mb - 64.5 51.9 77.1 80 58 67.2
YOLOv3-tiny - 5.4G 34.7Mb - 71.7 69.6 73.9 76 72 73.9

4.4 Visualization of experimental results

image

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            synthetic-fire-smoke_dataset,
                            title = { synthetic fire-smoke Dataset },
                            type = { Open Source Dataset },
                            author = { Yunnan University },
                            howpublished = { \url{ https://universe.roboflow.com/yunnan-university/synthetic-fire-smoke } },
                            url = { https://universe.roboflow.com/yunnan-university/synthetic-fire-smoke },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-04-26 },
                            }

Connect Your Model With Program Logic

Find utilities and guides to help you start using the synthetic fire-smoke project in your project.

roboflow templates thumbnail

supervision

Find utilities to work with this project in Python.

 roboflow templates thumbnail

Roboflow Learn

Build the knowledge you need to evaluate and deploy your model.

Source

Yunnan University

Last Updated

a year ago

Project Type

Object Detection

Subject

fire-smoke

Views: 5628

Views in previous 30 days: 1026

Downloads: 373

Downloads in previous 30 days: 55

License

CC BY 4.0

Classes

fire smoke
Object Detection Model yolov8s yolov8 yolov8m yolov8n