Multi‐step implicit Adams predictor‐corrector network for fire detection

Abstract

Fire detection methods based on the Convolutional Neural Networks (CNN) have advantages of high accuracy, wide coverage and robustness, receiving significant attention from researchers. Among CNN-based methods, ResNet has achieved better performance than other CNN frameworks in fire detection system, since it uses stacked residual blocks to enlarge the receptive field to overcome the vanishing gradient problem with residual learning. The merits of ResNet can be attributed to the similarity between ResNet and the single-step explicit solver for Ordinary Differential Equations (ODEs), for example, the Euler method. Motivated by the theory of numerical ODE that a multi-step implicit solver has higher accuracy than a single-step explicit solver, the Multi-step Implicit Adams predictor-corrector (MIAPC) network for fire detection is proposed. The MIAPC method is first mapped to a corresponding predictor-corrector Adams block which achieves higher accuracy than a single-step explicit solver. Then, Adaptive Feature Fusion (AFF) and the Spatial Attention Layer (SAL) are utilized to extract hierarchical features from stacked predictor-corrector Adams blocks, forming the corresponding Adams module. Finally, the 4 Adams modules which are made of 4, 6, 8, 10 predictor-corrector Adams blocks and followed by AFF and SAL form the crucial ODE-based approximation part in the proposed network. By adding a simple feature extraction and detection in front of and after the ODE-based approximation part, the MIAPC network is built. Experiments demonstrate that the method achieves 87% accuracy in the challenging test dataset, outperforming existing methods by at least 6%. Besides, the 5.3M model size with inference speed of 4.7 frames/second in CPU and 65.7 frames/second in GPU enables the proposed method to be used in practical applications.