Vision based model improves fire and smoke detection in outdoor areas
Attention-Based Spatio-Temporal Approach for Fire and Smoke Detection
Computer Vision and Pattern Recognition
Summary
Detecting fire and smoke is tricky, especially outdoors where usual sensors don't work well. The authors created a new method that looks at both the shapes in images and how these shapes change over time to spot smoke and fire. They used parts of AI called transformers to make the model more accurate. Their approach aims to help keep workplaces safer by detecting dangers early.
What this means in practice
- •For industrial safety teams: Incorporate the model to detect smoke and fire outdoors where traditional sensors fail, enhancing hazard alerts in industrial sites.
- •For smart city infrastructure teams: Deploy vision-based detection systems in public outdoor spaces to monitor and respond to fire hazards dynamically.
Authors
Alireza Mirrashid, Mohammad Khoshbin, Ali Atghaei, Hassan Shahbazi
Abstract
In various industries, smoke and fire are two of the most important threats in the workplace. One of the common methods for detecting smoke and fire is the use of infrared thermal and smoke sensors, which cannot be used in outdoor applications. Therefore, the use of vision-based methods seems necessary. The problem of smoke and fire detection is spatiotemporal and requires spatiotemporal solutions. This paper presents a method that uses spatial features along with temporal-based features to detect smoke and fire in the scene. It consists of three main parts; the task of each part is to reduce the error of the previous part so that the final model has a robust performance. This method also uses transformer modules to increase the accuracy of the model. The results of our model show the proper performance of the proposed approach in solving the problem of smoke and fire detection and can be used to increase workplace safety.