Smoke detection and trend prediction method based on Deeplabv3+ and generative adversarial network

Abstract

The detection of smoke in the initial stage is vital for preventing fire events. Therefore, we present a method of smoke heatmap detection using computer vision. First, the smoke region is segmented by encoder–decoder with atrous separable convolution (Deeplabv3+), and the edge of smoke is optimized with conditional random field to achieve pixel-level detection of early fire smoke. Subsequently, the heatmap of smoke thickness based on HSV or gray feature is established, and the space–time distribution of the smoke region is analyzed. In addition, generative adversarial network is used to predict the future frames and smoke trend heatmap, which will contribute to the development of fire protection and provide suggestions for rescue or evacuation. The experimental results show that the proposed method can accurately detect the fire smoke in different scenes and provide an effective heatmap analysis scheme, as well as provides basic data for further study on the trend of fire.