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

• Published in: Expert systems with applications Vol. 189; p. 116114
• Main Authors: Majid, Saima, Alenezi, Fayadh, Masood, Sarfaraz, Ahmad, Musheer, Gündüz, Emine Selda, Polat, Kemal
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01-03-2022; Elsevier BV
• Subjects: Attention mechanism; CNN; Computer vision; Fire detection; Grad-CAM; Localization; Transfer learning; Attention mechanism; CNN; Fire detection; Transfer learning; Grad-CAM
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.116114

•A custom deep learning framework for detecting fire in real-world images.•Attention mechanism and transfer learning is used with EfficientNetB0 trained.•Framework uses Grad-CAM method for visualization and localization of fire.•High recall of 97.61 supports the reliability of model for fire detection task.
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.