Efficient Smoke Detection Based on YOLO v5s

Hang Yin, Mingxuan Chen, Wenting Fan, Yuxuan Jin, Shahbaz Gul Hassan () and Shuangyin Liu ()
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Hang Yin: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Mingxuan Chen: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Wenting Fan: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Yuxuan Jin: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Shahbaz Gul Hassan: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China
Shuangyin Liu: College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou 510225, China

Mathematics, 2022, vol. 10, issue 19, 1-16

Abstract: Smoke detection based on video surveillance is important for early fire warning. Because the smoke is often small and thin in the early stage of a fire, using the collected smoke images for the identification and early warning of fires is very difficult. Therefore, an improved lightweight network that combines the attention mechanism and the improved upsampling algorithm has been proposed to solve the problem of small and thin smoke in the early fire stage. Firstly, the dataset consists of self-created small and thin smoke pictures and public smoke pictures. Secondly, an attention mechanism module combined with channel and spatial attention, which are attributes of pictures, is proposed to solve the small and thin smoke detection problem. Thirdly, to increase the receptive field of the smoke feature map in the feature fusion network and to solve the problem caused by the different smoke scenes, the original upsampling has been replaced with an improved upsampling algorithm. Finally, extensive comparative experiments on the dataset show that improved detection model has demonstrated an excellent effect.

Keywords: smoke detection; small and thin; lightweight network; attention mechanism; improved upsampling algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
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