A real-time detection model for smoke in grain bins with edge devices

Hang Yin; Mingxuan Chen; Yinqi Lin; Shixuan Luo; Yalin Chen; Song Yang; Lijun Gao

doi:10.1016/j.heliyon.2023.e18606

A real-time detection model for smoke in grain bins with edge devices

Heliyon. 2023 Jul 31;9(8):e18606. doi: 10.1016/j.heliyon.2023.e18606. eCollection 2023 Aug.

Authors

Hang Yin^{1

2}, Mingxuan Chen², Yinqi Lin², Shixuan Luo², Yalin Chen², Song Yang³, Lijun Gao⁴

Affiliations

¹ College of Big Data and Internet, Shenzhen Technology University, Shenzhen, 518118, China.
² College of Information Science and Technology, Zhongkai University of Agriculture and Engineering, Guangzhou, 510225, China.
³ College of Software, Dalian University of Foreign Languages, Dalian, 116044, China.
⁴ College of Computer Science, Shenyang Aerospace University, Shenyang, 110136, China.

Abstract

The global food crisis is becoming increasingly severe, and frequent grain bins fires can also lead to significant food losses at the same time. Accordingly, this paper proposes a model-compressed technique for promptly detecting small and thin smoke at the early stages of fire in grain bins. The proposed technique involves three key stages: (1) conducting smoke experiments in a back-up bin to acquire a dataset; (2) proposing a real-time detection model based on YOLO v5s with sparse training, channel pruning and model fine-tuning, and (3) the proposed model is subsequently deployed on different current edge devices. The experimental results indicate the proposed model can detect the smoke in grain bins effectively, with mAP and detection speed are 94.90% and 109.89 FPS respectively, and model size reduced by 5.11 MB. Furthermore, the proposed model is deployed on the edge device and achieved the detection speed of 49.26 FPS, thus allowing for real-time detection.

Keywords: Channel pruning; Edge devices; Grain bins; Real-time smoke detection; Sparse training.