Smoke detection plays an essential role in the wild video surveillance systems for abnormal events warning. In this paper, we introduced a dedicated neural network structure named Sniffer-Net to simultaneously extract smoke dynamic feature robustly and evaluate the smoke concentration accurately. Firstly, we utilize an improved LiteFlowNet to estimate the global optical flow from image sequence. Meanwhile, a Marr–Hildreth method is brought up and fused into this network to distinguish and eliminate occluded regions from global flow map. Then, an evaluation module based on Context-Encoder network is put forward specially to quantify smoke concentration levels. This network, following the improved LiteFlowNet, is modified through replacing the loss function and removing the multiscale scheme and trained to infer approximate smoke optical flow behind occlusion regions. Starting from the statistical view, the irregular RGB/HSV feature spaces are converted into a specific quantitative evaluation space. As a result, the whole evaluation system is responsible to transform the distribution of irregular smoke motion feature into a quantified form of representation. In turn, this transformation endows the system with a novel numerical standard for smoke concentration evaluation. Finally, an accuracy assessment method is applied to compare the results of detected smoke concentration with the human experience prior model, which feedback the accuracy and false detection rate of system algorithm. In the experiments of five smoke datasets, our proposed smoke detection approach is superior to other state-of-the-art methods, and concentration algorithm achieves the satisfactory performance of 97.3% accuracy on some specialized dataset.
相似文献In this paper, we design a novel low voltage of electroosmotic micromixer with fractal structure. Because of the influence of high voltage on electrode and solution, we propose an electroosmotic micromixer of low voltage. In order to optimize the electrode position, we design the Cantor fractal according to Cantor principle, and arrange the electrode pairs on the fractal. Then we study the mixing effect of the electrode pairs length on the mixing performance, the effect of the electrode position and the effect of fractal electrode group spacing on the mixing efficiency. When the electroosmotic micromixer has three electrode groups at alternating voltage of 5 V and alternating frequency of 8 Hz, the best mixing efficiency can reach 95.2% in one second. We call this micromixer Cantor fractal electroosmotic micromixer (CFEM). At the same Re, the mixing efficiency of CFEM is higher than the electrodeless micromixer 50%.
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