A deep learning framework for autonomous flame detection

This paper proposes a novel framework of flame region-based convolutional neural network for autonomous flame detection. The task of flame detection is especially challenging since flames have greater diversity in colour, texture, and shape than regular rigid objects. To cope with these difficulties due to the various appearances and unclear edges of flames, a proposal generation approach is developed to effectively select candidate flame regions based on two crucial properties of flames, i.e., their dynamics and colours. The candidate flame regions together with a convolutional feature map are further processed by additional layers to output detected flames. The diversity in flame colours is well represented by approximating the distribution using a Dirichlet Process Gaussian mixture model with variational inference. The proposed framework is evaluated on publicly available videos and achieves an average frame-wise accuracy higher than 88%, which outperforms the state-of-the-art methods.