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
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
6
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
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
19
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
21
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
22
Adam Optimizer with a learning rate of 0.0001
Batch size: 32
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
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