Smoke detection

Keywords: Smoke, Detection

Background

Smoke detection is an essential task for fire prevention and safety, and can benefit from the application of deep learning techniques. This paper focuses on utilizing deep learning methods for smoke detection. The problem statement for this work is to develop an accurate smoke detection model using deep learning techniques. The model will learn discriminative features to distinguish smoke from other objects or environmental elements. The results obtained using deep learning techniques for smoke detection have shown promising accuracy levels, highlighting the potential of these methods in accurately identifying smoke in various scenarios. Such models can be integrated into fire alarm systems or surveillance systems to improve early smoke detection, enabling timely response and potentially saving lives and property.

Aim & Objectives

Aim
To Detect the Smoke using DL Techniques
Objective
Improve smoke detection accuracy using ANN's pattern recognition. Create early warning systems for smoke incidents through ANN analysis of environmental data. Enable real-time smoke detection and alerts using ANN-based systems for enhanced safety.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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