earticle

영어

Fire detection is a significant attempt for preserving public safety in complex surveillance environments. Although advances in deep learning for fire detection, the task remains challenging due to the natural irregularity in fire images, including differences in lighting conditions, occlusions, and background complexity. To address these challenges, we present a novel framework for fire detection named fire channel attention network (FCAN), which is capable of differentiating challenging fire scenes. Our approach is motivated by the need to enhance the accuracy of fire detection by selectively emphasizing the most informative channels of the input image through a channel attention (CA). Furthermore, our model captures the salient features from the input image and suppresses the irrelevant ones, thereby overcoming the aforementioned challenges of fire detection. The FCAN is evaluated on two benchmark datasets and surpassed existing methods in terms of accuracy and F1 score. The proposed model demonstrates the effectiveness of fire detection, highlighting its potential for practical applications in fire safety and prevention.