Yolov5 for Fire Detection

Fire detection task aims to identify fire or flame in a video and put a bounding box around it. This repo includes a demo on how to build a fire detection model using Yolov5.

Setup

Clone this repo and use the following script to install yolov5. Then download Fire-Dataset and put it in datasets folder. This dataset contains samples from both Kaggle Fire & Smoke and Fire & Guns datasets. I filtered out images and annotations that contain smokes & guns as well as images with low resolution, and then changed fire annotation's label in annotation files.

# Clone this repo
git clone https://github.com/spacewalk01/Yolov5-Fire-Detection

# Install yolov5
git clone https://github.com/ultralytics/yolov5  
cd yolov5
pip install -r requirements.txt  # install

Use train.ipynb or the following commands for training & inference.

• Train

python train.py --img 640 --batch 16 --epochs 10 --data ../fire_config.yaml --weights yolov5s.pt --workers 0

• Inference

python detect.py --source input.mp4 --weights runs/train/exp/weights/best.pt --conf 0.2

Result

The following diagrams were produced after training yolov5s with input size 640x640 on the fire dataset 10 iterations. [Update Soon]

P Curve	PR Curve	R Curve

Prediction Results

The fire detection results were fairly good even though the model was trained only a few iterations. However, I observed that the trained model tends to predict red emergency light on top of police car as fire. It might be due to the fact that the training dataset contains only a few hundreds of negative samples. I presume that we can fix this issue and further improve the performance of the model by adding images with non-labeled fire objects as negative samples.

Ground Truth	Prediction

Feature Map Visualization

It is desirable for engineers to know what happens under the hood of object detection models. Visualizing features in deep learning models can help us a little bit understand how they make predictions. In Yolov5, we can visualize features using --visualize argument as follows:

python detect.py --weights runs/train/exp/weights/best.pt --img 640 --conf 0.2 --source ../datasets/fire/val/images/0.jpg --visualize

Input	Feature Maps

Reference

I borrowed and modified YOLOv5-Custom-Training.ipynb script for training Yolov5 model on the fire dataset. For more information on training yolov5, please refer to its homepage.

• https://github.com/robmarkcole/fire-detection-from-images
• https://github.com/ultralytics/yolov5