FIRE IMAGE DETECTION USING CNN
Nilesh Tanna*, Nishant Govekar*, Samruddhi Chavan*, Fahad Ansari*, Prof.Vedika Avhad*
Student, Department of Information Technology, Vasantdada Patil Pratishthan’s College of Engineering and Visual Arts, Mumbai, India
Assistant Professor, Department of Information Technology, Vasantdada Patil Pratishthan’s College of Engineering and Visual Arts, Mumbai, India
A B S T R A C T
Fire detection is a critical task in ensuring the safety of human lives and property. In recent years, deep learning techniques, such as Convolutional Neural Networks (CNNs), have shown promising results in fire detection due to their ability to automatically learn relevant features from images. In this project, we propose a real-time fire detection system using CNNs to detect fires in images or video streams.
The proposed system consists of a CNN-based architecture that includes multiple convolutional layers for feature extraction, activation functions for introducing non-linearity, pooling layers for spatial dimension reduction, and fully connected layers for global pattern learning. The architecture is trained on a labelled dataset of fire and non-fire images or videos using optimization algorithms to minimize the loss function.
To achieve real-time fire detection, the trained CNN model is integrated into a fire detection system that can process images or video streams in real-time. The system captures images or video frames from a live feed, pre-processes the data, and passes it through the CNN model for fire detection. The system can generate alerts or notifications in real-time when fire is detected, allowing for prompt response and mitigation measures.
The performance of the proposed fire detection system is evaluated using metrics such as accuracy, precision, recall, and F1-score. The system is tested on different datasets and under various conditions to assess its accuracy, robustness, and real-time processing capabilities.
The results of this project demonstrate the effectiveness of CNNs for real-time fire detection and highlight the potential of the proposed system for enhancing fire safety in various applications, including surveillance systems, smart buildings, and industrial settings. The developed system can be used as an early warning tool for fire detection, enabling timely response and mitigation, and helping to prevent or minimize the damage caused by fires.
Keywords: Fire detection, image classification, real-world applications, deep learning, and CCTV video analysis.
