A lightweight smoke detection network incorporated with the edge cue

Smoke detection is essential for disaster management and timely response to fires. However, popular deep learning-based algorithms ignore key traditional features such as color and texture, causing limitations in detection. Therefore, a lightweight edge-guided smoke detection network (ESmokeNet) is proposed in this paper. First, edge cues are refined from the low-level maps and integrated into the network features according to the color channels. Then, to increase the smoke feature extraction capability, we design a mutual context embedding module (MCE). It enhances multiscale context and multilevel semantic information based on attention and chiral features. Finally, the classification and localization tasks are decoupled to pursue the usability of network features directed by smoke edges, and an EdgeDet head is proposed. Extensive experimental and visualization results have confirmed that ESmokeNet can successfully capture smoke edges and that it has significant superiority over the existing state-of-the-art methods and specialized smoke detection algorithms. In addition, the test data show that our ESmokeNet is only 5.42 M and is a lightweight network suitable for smoke detection tasks. Code is available at https://github.com/jingjing-maker/ESmokeNet.

Automated summary

This paper proposes a lightweight edge-guided smoke detection network (ESmokeNet) to improve smoke detection by integrating edge cues and enhancing smoke feature extraction capability. Experimental results show that ESmokeNet has significant superiority in capturing smoke edges and is a lightweight network suitable for smoke detection tasks.