Real time fire detection using YOLOv5
Kanala Harika, Rishabh Ravindran
YOLOv5, Fire detection, Bounding Box, Deep Learning, Twilio, Versatile, Text Alerts.
Using a YOLOv5 model for fire detection and a bounding box to constrain the extent of the detected fire, our project's objective is to detect fires with high precision and reduce false alarms. By doing so, we can ensure that only significant fires are reported and reduce the number of false alarms that squander precious resources. Moreover, our initiative allows the fire detection system to be utilized in any environment where fire is a significant factor. Twilio, a cloud-based service, allows us to send text alerts when a fire is detected based on the environment's configured parameters. With the aid of deep learning technology, our fire detection system can detect fires rapidly and precisely, allowing us to take prompt and appropriate action. Our project's use of bounding boxes for fire detection is a novel approach that can substantially reduce false alarms and improve the system's accuracy compared to similar IoT or deep learning projects. Moreover, the adaptability of our project to use the fire detection system in any environment renders it highly versatile, making it an ideal solution for numerous industries. Overall, the combination of our project's high accuracy, reduced false alarms, and adaptability makes it superior to comparable IoT or deep learning projects currently available.
Article Details
Unique Paper ID: 162870

Publication Volume & Issue: Volume 10, Issue 11

Page(s): 259 - 267
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