Abstract
Smoke detection can warn fires and prevent them. Visual-based smoke detection has gained more attention. However, there is a lack of publicly available smoke datasets in this area. To fill this gap, in this paper, we build a smoke dataset based on various realistic environments that contains 24776 smoke RGB images. Furthermore, a Contextual Interaction Enhancement Network for smoke detection is presented. Re-parameterized large kernel convolutions are utilized in the backbone to increase the receptive field of smoke feature extraction. And a Contextual Interaction Enhancement Module (CIEM)-neck is proposed to get aggregated features for better feature assignment to the decoupled head. Experimental results show that our method has excellent performance, achieving state-of-the-art results on the proposed Smoke 24776 dataset at 83.8% precision, 77.3% AP@0.5 and 49.8% mAP. Additionally, even if it is specialized for smoke detection, our method has still competitive performance and generalization on famous MS-COCO with 66% AP@0.5 and 47.0% mAP. Dataset is available at https://github.com/linjiefengFutureMediaSZU/Smoke24776.