融合多分辨率表征的实时烟雾分割算法

针对烟雾分割领域缺乏应用于实际监控系统的实时烟雾分割算法的现况，提出高准确率的实时烟雾分割算法. 该算法利用轻量化的多分辨率卷积模块并行提取特征图，在获得丰富语义信息的同时满足实时分割的需求. 提出烟雾前景增强模块，使得烟雾像素点融合前景增强表征、避免背景信息干扰，分割准确率得以提高. 提出残差注意力模块，从通道、空间维度增强重要特征信息，抑制无效信息. 该算法在自建数据集上平均交并比为91.27%，每张图片预测时间为39.06 ms，网络权重为74.66 MB；在公开数据集上的对比结果表明，该算法综合检测性能优于其他烟雾检测算法. 该算法分割准确率高、检测速度快且模型轻量化，可以应用于实际视频监控系统.

Real-time smoke segmentation algorithm fused with multi-resolution representation

A high-accuracy real-time smoke segmentation algorithm was proposed, aiming at the lack of a real-time smoke segmentation algorithm applied to the actual monitoring systems in the field of smoke segmentation. A lightweight multi-resolution convolution module to extract feature maps in parallel was used in the algorithm, which met the needs of real-time segmentation while obtaining rich semantic information. A smoke foreground enhancement module was proposed to enable smoke pixels to be merged with their corresponding foreground enhancement representations, while avoiding the interference of background information, thereby improving the accuracy of segmentation. A residual attention module was proposed to enhance important feature information from the two dimensions of channel and space, and suppress invalid information. The algorithm had a mean intersection over union of 91.27% on the self-built data set, the prediction time of each picture was 39.06 ms, and the network weight was 74.66 MB. Comparison results on the public data set show that the comprehensive detection performance of this algorithm is better than that of other smoke detection algorithms. The algorithm has high segmentation accuracy, fast detection speed and the model is lightweight, which can be applied to actual video surveillance systems.