Research On Forest Fire Smoke Identification

In recent years,China is facing an increasingly serious threat of forest fires.This paper studies the forest fire smoke identification algorithm based on computer vision technology from four key aspects: image preprocessing,moving target detection,single frame recognition method and multi-frame recognition method.(1)Research on image dehaze algorithm.In this paper,an improved fast dehaze algorithm is designed for the problem that dark channel prior algorithm is prone to generate distortion noise block in sky region.The Sobel operator is used to detect edge in original image and segment the sky region which needs more detailed dehaze operations.Secondly,the ambient light and the atmospheric light are directly estimated from the original image,which greatly improves the execution efficiency of the dehaze algorithm.Experiments show that the improved fast dehaze algorithm can meet the engineering application of forest fire surveillance system.(2)Research on motion detection algorithm.The ViBe algorithm cannot adapt to depth changes in forest monitoring scenarios.In this paper,an adaptive ViBe algorithm combining depth information is designed.The depth information is calculated by the dark channel prior theory.A mapping function is designed to adjust the detection sensitivity of ViBe moving target detection method at different depths.Experiments show that the improved fast dehaze algorithm improves the robustness of forest fire smoke detection without losing the efficiency of the algorithm.(3)Research on single-frame forest fire smoke identification method based on deep learning.A convolutional neural network model based on attention mechanism is designed.The discriminative part in image is located through the attention network.The sub-branch network is used to extract finer local features from discriminative part,which are used to assist the backbone network in classification.Secondly,the weighted cross-entropy loss function is optimized to improve the performance of the network on the unbalanced forest fire smoke dataset.The experimental results show that the convolutional neural network based on attention mechanism has a great improvement in accuracy and model stability.(4)Research on multi-frame forest fire smoke identification method based on deep learning.A bidirectional LSTM network was designed to extract the dynamic characteristics from the sequence.Firstly,the static features are extracted by the attentional convolutional neural network,and the features are input into the bidirectional long-term and short-term memory networks in both forward and reverse directions for cyclic calculation.Finally,more reliable predictions are made based on the forward and reverse states in the network.The experimental results show that the proposed method achieves the best classification accuracy on the sequence dataset.