A boundary enhancement and pixel alignment based smoke segmentation network

Abstract

Image segmentation methods usually fuse shallow and deep features to locate object boundaries, but it is difficult to improve the accuracy of smoke segmentation by conventional fusion methods. It is a very difficult vision task to perform semantic segmentation of smoke images, because the translucency and irregular shapes of smoke lead to extremely complicated mixtures with background that are adverse to segmentation. To improve the segmentation accuracy of smoke scenes, we propose a Boundary Enhancement and Pixel Alignment based smoke segmentation Network for fire alarms. For the shallow features of the network, an attention mechanism is adopted to capture spatially details of smoke for improving boundary precision. For the deep layers, the Pyramid Pooling Module is used to extract local features and abstract semantic ones simultaneously. Finally, to efficiently merge shallow and deep features, a Pixel Alignment Module is adopted to model the relationship between pixel locations. The experimental results show that the mean Intersection over Union of the proposed method on the three synthetic smoke test datasets is 78.61%, 77.63% and 77.30%, respectively, and it outperforms most of the existing methods. In addition, our method obtains satisfying results on inconspicuous smoke and smoke-like images.