Mechanism (biology)

Quantum mechanics

Linguistics

Philosophy

Object detection

Transfer of learning

Breakout

Visualization

Economics

Power (physics)

Computer science

Recall

Thermodynamics

Physics

Task (project management)

Pattern recognition (psychology)

Management

Image (mathematics)

Epistemology

Reliability (semiconductor)

Artificial intelligence

Deep learning

Fire detection

Finance

Machine learning

Computer vision

Expert Systems with Applications

Fire is a severe natural calamity that causes significant harm to human lives and the environment. Recent works have proposed the use of computer vision for developing a cost-effective automated fire detection system. This paper presents a custom framework for detecting fire using transfer learning with state-of-the-art CNNs trained over real-world fire breakout images. The framework also uses the Grad-CAM method for the visualization and localization of fire in the images. The model also uses an attention mechanism that has significantly assisted the network in achieving better performances. It was observed through Grad-CAM results that the proposed use of attention led the model towards better localization of fire in the images. Among the plethora of models explored, the EfficientNetB0 emerged as the best-suited network choice for the problem. For the selected real-world fire image dataset, a test accuracy of 95.40% strongly supports the model's efficiency in detecting fire from the presented image samples. Also, a very high recall of 97.61 highlights that the model has negligible false negatives, suggesting the network to be reliable for fire detection.

Attention based CNN model for fire detection and localization in real-world images