IoT and CNN Based Fire Detection And Prevention

Fire incidents pose significant risks to life and property, necessitating the development of advanced fire detection and suppression systems. This paper presents an innovative approach that combines a Convolutional Neural Network (CNN)- based image processing model with hardware integration for real- time fire detection and immediate response actions. The model is based on Yolo-v4 and techniques Cross validation and Grey Level Co-occurrence and median filtering are used for training of the model. The system utilizes a webcam for image capture, employing a CNN model trained on a large dataset to accurately identify fire conditions. Hardware components including temperature and humidity sensors (DHT11) and flame sensors complement the image processing for enhanced accuracy and reliability. The smoke sensors are usually used widely, but the flame sensors have a quicker response time. The proposed system demonstrates exceptional performance, achieving a detection accuracy 98%. Upon detecting fire, the system triggers an alarm through a buzzer for immediate evacuation alerts and activates a water motor for fire suppression. This integrated approach ensures rapid response to fire incidents, minimizing potential damages and enhancing overall safety. Key system features include scalability, adaptability, and user-friendliness, suitable for diverse environments like residential buildings, industries, and public spaces. Future upgrades might integrate advanced cameras, use machine learning for adaptive responses, and implement continuous monitoring for false positives, enhancing system capabilities. Overall, the developed system represents a significant advancement in leveraging technology for fire safety, with promising implications for mitigating fire-related risks effectively.