aamiralidev / fire-fighting-robot

Firefighting Robot

Introduction

Fire poses a significant threat to human life and property. Firefighters often face life-threatening situations, especially in hazardous environments like nuclear power plants and oil refineries. This project introduced a firefighting robot designed to enhance safety and efficiency in combating fires. The robot leveraged deep learning techniques for fire detection and autonomous firefighting.

Goals

1. Developed a deep learning-based fire detection system.
2. Implemented and evaluated various deep-learning models for fire detection.
3. Assessed model performance under different conditions and environments.
4. Built an autonomous firefighting robot capable of real-time fire detection and response.

Project Scope

This project encompassed the following key aspects:

• Data collection: Gathered a diverse dataset of fire and non-fire images or videos for model training.
• Data preprocessing: Prepared and cleaned the dataset for use with deep learning models.
• Model selection: Chose multiple state-of-the-art relevant deep learning models for fire detection.
• Model training: Trained the selected models on the annotated dataset.
• Model evaluation: Assessed the model's performance using accuracy, precision, recall, and F1 score metrics.
• Model optimization: Fine-tuned the models for improved accuracy and efficiency.
• Integration: Selected one best and combined it with a navigation and control system for the firefighting robot.
• Testing and validation: Thoroughly tested the firefighting robot's ability to detect fires and respond effectively.

Methodology

To achieve the project goals, we followed these key steps:

1. Data Collection: Curated a dataset of fire and non-fire images and videos.
2. Data Preprocessing: Prepared and annotated the dataset for training.
3. Model Selection: Chose multiple deep learning models suitable for fire detection.
4. Model Training: Trained the selected models on the annotated dataset.
5. Model Evaluation: Assessed the model's performance using accuracy, precision, recall, and F1 score.
6. Model Optimization: Fine-tuned the model for improved accuracy and efficiency.
7. Integration: Combined the fire detection model with a robot control system.
8. Testing and Validation: Rigorously tested the firefighting robot's ability to detect and respond to fires.

Evaluation Metrics

We evaluated the model's performance using the following metrics:

• Accuracy: Measured the percentage of correctly identified fires.
• Precision: Measured the percentage of true positives among positive predictions.
• Recall: Measured the percentage of true positives among actual fires.
• F1 Score: Calculated the harmonic mean of precision and recall.

Conclusion and Future Work

In this project, we revolutionized firefighting by introducing an autonomous firefighting robot equipped with deep learning-based fire detection capabilities. The potential benefits included enhanced safety and faster response times in hazardous environments.

Future work may involve incorporating additional sensors for increased accuracy, as well as expanding the robot's capabilities for more complex firefighting scenarios.

For detailed documentation or any inquiries, you can request the project report.