Enhanced Fire Detection in Trains Using Generative Adversarial Networks

Abstract

Traditional fire detection systems in trains, often relying on smoke detectors and heat sensors, can be limited by false alarms, delayed detection, and reduced accuracy in challenging environments. This invention addresses these limitations by introducing a novel approach that leverages the power of Generative Adversarial Networks (GANs) to significantly enhance fire detection accuracy and reliability. The proposed system comprises a GAN model, a fire detection model, and a real-time video processing pipeline. The GAN model generates highly realistic synthetic fire images, capturing various fire stages, smoke patterns, and lighting conditions. This synthetic data, combined with real-world fire images, is used to train the fire detection model, a deep convolutional neural network (CNN), to accurately identify fire patterns, even in low-light and obscured conditions. The real-time video processing pipeline captures and processes video feeds from train compartments. Each frame is pre-processed to enhance fire-related features, such as contrast and edge detection, and then fed to the fire detection model for analysis. The model analyzes the input frame and generates a probability score indicating the likelihood of fire. If the score exceeds a predefined threshold, an alarm is triggered, alerting the train crew and passengers. Additionally, the system can activate automatic fire suppression systems and notify relevant authorities. By combining the power of GANs and advanced deep learning techniques, this invention offers a robust and effective solution for fire detection in trains. It enhances safety by providing early warning and rapid response to fire incidents, protecting passengers and crew, and minimizing potential damage.

Specification

Description:Field of the Invention
[001] This invention pertains to the field of railway safety and security, specifically focusing on the development of advanced fire detection systems. More specifically, this invention relates to the use of Generative Adversarial Networks (GANs) to improve the accuracy and reliability of real-time fire detection in train compartments. The invention aims to address the limitations of traditional fire detection systems by providing a robust, accurate, and early warning system for fire incidents, even in challenging environmental conditions and low-light scenarios.
Background
[002] Traditional fire detection systems, such as smoke detectors and heat sensors, have been widely used in trains. However, these systems often suffer from limitations such as false alarms, delayed detection, and limited accuracy, particularly in environments with fluctuating temperature and humidity.
[003] Recent advancements in artificial intelligence and computer vision have opened up new possibilities for improving fire detection systems. Deep learning techniques, such as convolutional neural networks (CNNs), have been applied to image-based fire detection. However, these methods require large amounts of labeled training data, which can be challenging to obtain, especially for rare events like fires.
Summary of the Invention
[004] This invention provides a novel approach for enhanced fire detection in trains using Generative Adversarial Networks (GANs). The GAN-based system is capable of generating diverse and realistic synthetic fire images, which can be used to augment the training data and improve the accuracy of the fire detection model.
[005] The system comprises a GAN model, a fire detection model, and a real-time video processing pipeline. The GAN model generates high-quality synthetic fire images, capturing various fire stages, smoke patterns, and lighting conditions. The fire detection model is trained on a combination of real and synthetic images to accurately identify fire patterns, even in low-light and obscured conditions. The real-time video processing pipeline captures and processes video feeds from train compartments, feeding the frames to the fire detection model for analysis.
Detailed Description
[006] The GAN-based fire detection system operates as follows:
1. GAN Training:
o A dataset of real fire images is collected, covering various fire scenarios, including different types of materials, stages of fire, and lighting conditions.
o The GAN model is trained on this dataset to learn the underlying distribution of fire images.
o The generator component of the GAN learns to produce high-quality, diverse, and realistic fire images, while the discriminator component learns to distinguish between real and synthetic images.
2. Fire Detection Model Training:
o A large dataset of real and synthetic fire images is created, along with a dataset of non-fire images.
o The fire detection model, typically a deep convolutional neural network (CNN), is trained on this combined dataset.
o The model learns to extract relevant features from the images, such as color, texture, and motion patterns, to accurately classify them as fire or non-fire.
3. Real-time Video Processing:
o The real-time video processing pipeline captures and processes video frames from train compartments.
o Each frame is pre-processed to enhance fire-related features, such as contrast and edge detection.
o The pre-processed frames are fed to the fire detection model for analysis.
4. Fire Detection and Alarm:
o The fire detection model analyzes the input frame and generates a probability score indicating the likelihood of fire.
o If the score exceeds a predefined threshold, an alarm is triggered, alerting the train crew and passengers.
o Additionally, the system can provide visual and auditory alerts, as well as activate automatic fire suppression systems.
, Claims:1. A method for enhanced fire detection in trains, comprising:
a. Training a Generative Adversarial Network (GAN) to generate realistic fire images, including various fire stages, smoke patterns, and lighting conditions;
b. Training a fire detection model on a diverse dataset comprising real and synthetic fire images, as well as non-fire images, to improve robustness and accuracy;
c. Capturing and pre-processing video frames from train compartments, including techniques like noise reduction, contrast enhancement, and edge detection;
d. Feeding the pre-processed frames to the fire detection model for analysis;
e. Detecting fire in the video frames based on the output of the fire detection model, considering factors like fire intensity, smoke density, and flame propagation; and
f. Triggering an alarm, activating fire suppression systems, and notifying relevant authorities in case of fire detection.
2. The method of claim 1, wherein the GAN model is trained using a dataset of real fire images captured under various environmental conditions, including low-light, smoke-filled, and obscured scenarios.
3. The fire detection model of claim 1, comprising a deep convolutional neural network (CNN) architecture with multiple layers, including convolutional, pooling, and fully connected layers, 1 for feature extraction and classification.
4. A fire detection system for trains, comprising:
a. A network of video cameras strategically placed within train compartments to capture video feeds;
b. A processing unit for real-time video processing, including frame extraction, pre-processing, and feature extraction;
c. A fire detection module implementing the method of claim 1;
d. An alarm system for alerting the train crew and passengers in case of fire detection; and
e. A communication module for transmitting fire alerts to remote monitoring centers.
5. The fire detection system of claim 4, further comprising a fire suppression system that can be activated automatically or manually to mitigate fire damage.

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