A Hybrid Deep Learning and Computer Vision  Approach to Intelligent Fire Detection in  Buildings

Yadamakanti Nithin; Sathvik Patibandla; Syed Naseera; Keerthi Glory

A Hybrid Deep Learning and Computer Vision Approach to Intelligent Fire Detection in Buildings

Author(s)	Mr. Yadamakanti Nithin, Mr. Sathvik Patibandla, Ms Syed Naseera, Ms Keerthi Glory
Country	India
Abstract	It is still difficult to prevent accidents in commercial, industrial and residential buildings because fire detection has not caught up with newer technology. Heat and smoke detectors, among standard systems, often give out false alarms, cover only certain parts and react relatively slowly. To detect fires as soon as possible and with the highest accuracy, this work presents a mixed fire detection approach combining deep learning and computer vision functions. The system identifies a fire in two parts: it categorizes fire using a Convolutional Neural Network for context and analyses colors in HSV segments to look for signs of a fire. Before validating any warnings, a module reviews how long the fire lasts in variousframes to avoid mistaking other events for fires. With its hybrid design, the camera responds well to fires and manages to avoid identifying things that shine such as lights and reflections, as fires. Because the system was built in Python and set up in a Google Colab environment, it allows forthe submission of images or videos and gives live overlays and scores for whether an alarm should be triggered. A built-in dashboard provides fire zone maps, highlights new threats and their likely development. Use of the integrated method in more than 500 experiments showed that it decreased false alarms and detected fires in all test sequences where a fire occurred. To help society be more aware of fire safety, the system also includes fire safety teaching modules. Currently, the system suggested is helpful and adaptable due to its flexible design, continuous responsiveness and support for many platforms.
Keywords	Fire Detection, Deep Learning, Computer Vision, Convolutional Neural Network, HSV Colour Segmentation, Real-Time Monitoring, Hybrid Model, Smart Safety Systems, Temporal Analysis, Image Processing
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-25

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A Hybrid Deep Learning and Computer Vision Approach to Intelligent Fire Detection in Buildings

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