The document presents a deep learning-based fire and smoke detection system. It proposes a convolutional neural network (CNN) architecture for feature extraction from images to classify scenes as containing fire, smoke, or neither. The CNN was trained on a dataset of over 5000 images across three classes and achieved 96.3% accuracy for fire/smoke detection over 100 epochs. Future work could focus on improving model accuracy through additional data and real-time recognition capabilities. The system shows potential for computer vision-based fire detection as an alternative to traditional sensor-based methods.

1. DeepLearning-Based Fire andSmokeDetection
System
Presented by
S.AbijahRoseline
Paperid. 1354
Department ofComputational Intelligence
SRMInstitute ofScienceand Technology- Kattankulathur Campus
Chennai, India
22-02-2024

2. Outline
• Introduction
• Fire/Smoke Detection System
• Motivation
• Literature Survey
• Proposed Model
• Dataset
• Experimental Study and Results
• Conclusion and Futureworks
• References
2

3. Introduction
• Fire is the most perilous and hazardous abnormal event with the potential for
significant human, structural, and economic losses.
• Even a small fire can have a severe impact on temperature-sensitive industries
like Chemical, Oil, and Gas.
• Fire and smoke detection is crucial for public safety and property damage
prevention.
• Recent progress in computer vision and deep learning enables the development
of accurate detection systems.
• The proposed fire and smoke detection model, trained on a specialized dataset,
effectively identifies fire and smoke incidents.
3

4. Fire and Smoke Detection
System
4

5. Motivation
 Fire disasters are significant threats to human lives and
property.
 Limitations in traditional sensor-based methods.
 Sensor deployment is impractical in certain scenarios.
 Challenges arise when attempting to adapt to vast and
complex environments, particularly in large, open spaces.
 Other challenges include
 variations in lighting conditions,
 color overlap between fire and similar objects
 the complexity of detecting fire at its initial stage, especially on
extremely sunny days
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6. Literature Survey
Authors Method
Frizzi et al. Convolutional Neural Network
Praveen et al. Convolutional Neural Network
Khan et al. Fine-tuning of Convolutional Neural
Network
Salim et al. Inception-V2 and Faster R-CNN
Nori et al. YOLO (You Only Look Once)
Suhas et al. Simplified YOLO version

7. Proposed Model
7
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8. Proposed CNN Architecture

9. Proposed CNN Architecture
• Architecture includes convolutional layers for feature
extraction, capturing corners and edges.
• Rectified Linear Unit (ReLU) activation nullifies negative
inputs in the convolutional layer.
• Pooling layers optimize efficiency, reducing spatial
volume after convolution.
• Max pooling operates independently between
convolution layers on each feature map.
• Fully connected layers and softmax/logistic layer for
image classification and final output.

10. Dataset
Fire and smoke dataset
Total Images: 5046
• Videos: 38
• Classes:
Fire - 1735
Smoke - 1594
Non-Fire – 1717
• Train-Test ratio: 70:30

11. Experimental Setup
• Software
– Python framework
– Keras and Tensorflow deep learning library
• Hardware
– Windows 10 operating system utilizing a Ryzen 5
processor
– AMD Graphics 3500U GPU
– minimum of 8GB RAM
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12. Experimental Objectives
• To assess the performance of the proposed CNN model
for classifying malware using
Accuracy
Loss
Precision
Recall
F1-score
• To visualize the classification performance using
Accuracy/Loss graph
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13. Experimental Results
Dataset No. of
Epochs
Accuracy
(%)
Loss Precision Recall F1-
Fire and
Smoke
dataset
50 95.67 0.19 0.96 0.96 0.96
100 96.23 0.19 0.96 0.96 0.96
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Adam Optimizer with a learning rate of 0.0001
Batch size: 32

14. Accuracy/Loss Graphs
Higher accuracy
Lower loss
Does not suffer overfitting
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15. Fire Detection by the Proposed
System

16. Outputs for Non-Fire/Smoke
Detection

17. Conclusion and Future Work
• Fire/Smoke are detected from videos using proposed CNN.
• The proposed CNN produced promising results with an
accuracy of 96.3% for 100 epochs.
• As future work, precise real-time recognition can be
concentrated
• Also, Challenges in model accuracy arise due to the scarcity
of fire and smoke datasets, with only less number of images
available for training

18. [1] Sebastien Frizzi,Rabeb Kaabi, Moez Bouchouicha,Jean-Marc Ginoux, Eric Moreau and
Farhat Fnaiech. ”convolutional neural network for video fire and smoke detection”. IEEE
conference, pp.877-882, 2016.
[2] Praveen Sankarasubramanian and Dr. Ganesh.E.N. ”CNN based Intelligent Framework to
Predict and Detect Fire”. vol.20:pp.755–772, 2022.
[3] Khan muhammad , jamil ahmad,irfan mehmood , seungmin rho andsung wook baik.
”Convolutional Neural Networks Based Fire Detection in Surveillance Videos”. IEEE
conference, vol.6:pp.18174–18183, 2018.
[4] Senthilnayaki, B., Venkatalakshami, K., Dharanyadevi, P., Nivetha, G., & Devi, A. (2022,
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International Conference on Smart Technologies and Systems for Next Generation Computing
(ICSTSN) (pp. 1-5). IEEE
[5] Salim Said AL-Ghadani and Jayakumari.c. ”Innovating Fire Detection System Fire using
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Exploring Engineering (IJITEE) ISSN: 2278-3075 (Online), vol.9:pp.349–356, 2020.
[6] Ruba Nori.R, Rabah N. Farhan and Safaa Hussein Abed. ”Indoor and outdoor fire
localization using YOLO algorithm”. Journal of Physics:Conference Series, pp.1-10, 2021.
18
References

19. References
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20. Thank You