RESUMO
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.
