Multi-Scale Semantic Segmentation for Fire Smoke Image Based on Global Information and U-Net

Abstract

Smoke is translucent and irregular, resulting in a very complex mix between background and smoke. Thin or small smoke is visually inconspicuous, and its boundary is often blurred. Therefore, it is a very difficult task to completely segment smoke from images. To solve the above issues, a multi-scale semantic segmentation for fire smoke based on global information and U-Net is proposed. This algorithm uses multi-scale residual group attention (MRGA) combined with U-Net to extract multi-scale smoke features, and enhance the perception of small-scale smoke. The encoder Transformer was used to extract global information, and improve accuracy for thin smoke at the edge of images. Finally, the proposed algorithm was tested on smoke dataset, and achieves 91.83% mIoU. Compared with existing segmentation algorithms, mIoU is improved by 2.87%, and mPA is improved by 3.42%. Thus, it is a segmentation algorithm for fire smoke with higher accuracy.

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