Fire and Smoke Detection for Wildfire using YOLOV5 Algorithm

This research paper presents a realtime fire and smoke detection system using the YOLOv5 object detection algorithm. The system aims to detect fire and smoke in images and video streams captured by a camera in real-time, without the need for any preprocessing or manual intervention. The proposed system uses the YOLOv5 algorithm to detect the fire and smoke regions in the input images and videos. The YOLOv5 model is trained on a dataset of annotated images to recognize fire and smoke patterns accurately. The proposed system has been tested on different datasets and has achieved high accuracy and precision in detecting fire and smoke in real-time. The experimental results demonstrate that the proposed system is robust and efficient, and it can detect fire and smoke in real-time with high accuracy and low latency. The proposed system can be used in various applications, such as early warning systems, fire safety, and disaster management. It can also be integrated with the CCTV network directly.The testing findings show how reliable and effective the suggested system is, as well as how accurately and quickly it can detect fire and smoke in real time. Numerous applications, including early warning systems, fire safety, and disaster management, are suitable for the suggested system. Direct integration with the CCTV network is another option.