Lightweight and efficient octave convolutional neural network for fire recognition

Fire recognition from visual scenes is a demanding task due to the high variance of color and texture. In recent years, several fire-recognition approaches based on deep learning methods have been proposed to overcome this problem. However, building deep convolutional neural networks usually involves hundreds of layers and thousands of channels, thus requiring excessive computational cost, and a considerable amount of data. Therefore, applying deep networks in real-world scenarios remains an open challenge, especially when using devices with limitations in hardware and computing power, e.g., robots or mobile devices. To address this challenge, in this paper, we propose a lightweight and efficient octave convolutional neural network for fire recognition in visual scenes. Extensive experiments are conducted on FireSense, CairFire, FireNet, and FiSmo datasets. In overall, our architecture comprises fewer layers and fewer parameters in comparison with previously proposed architectures. Experimental results show that our model achieves higher accuracy recognition, in comparison to state-of-the-art methods, for all tested datasets.