Deep Smoke Segmentation

TL;DR

This paper introduces a deep smoke segmentation network that combines coarse and fine paths for accurate smoke mask inference from blurry images, utilizing synthetic data generation and end-to-end training.

Contribution

It presents a novel dual-path FCN architecture with a fusion network for improved smoke segmentation and a synthetic data generation method for training.

Findings

Outperforms state-of-the-art segmentation algorithms on synthetic datasets

Achieves accurate smoke detection in videos

Effective in handling variations in smoke appearance

Abstract

Inspired by the recent success of fully convolutional networks (FCN) in semantic segmentation, we propose a deep smoke segmentation network to infer high quality segmentation masks from blurry smoke images. To overcome large variations in texture, color and shape of smoke appearance, we divide the proposed network into a coarse path and a fine path. The first path is an encoder-decoder FCN with skip structures, which extracts global context information of smoke and accordingly generates a coarse segmentation mask. To retain fine spatial details of smoke, the second path is also designed as an encoder-decoder FCN with skip structures, but it is shallower than the first path network. Finally, we propose a very small network containing only add, convolution and activation layers to fuse the results of the two paths. Thus, we can easily train the proposed network end to end for simultaneous optimization of network parameters. To avoid the difficulty in manually labelling fuzzy smoke objects, we propose a method to generate synthetic smoke images. According to results of our deep segmentation method, we can easily and accurately perform smoke detection from videos. Experiments on three synthetic smoke datasets and a realistic smoke dataset show that our method achieves much better performance than state-of-the-art segmentation algorithms based on FCNs. Test results of our method on videos are also appealing.