An Improved Forest Fire and Smoke Detection Model Based on YOLOv5

Forest fires are destructive and rapidly spreading, causing great harm to forest ecosystems humans. Deep learning techniques can adaptively learn extract features of smoke. However, the complex backgrounds different fire smoke in captured images make detection difficult. Facing background smoke, it is difficult for traditional machine methods design a general feature extraction module extraction. effective many fields, so this paper improves on You Only Look Once v5 (YOLOv5s) model, improved model has better performance First, coordinate attention (CA) integrated into YOLOv5 highlight targets improve identifiability features. Second, we replaced YOLOv5s original spatial pyramidal ensemble fast (SPPF) with receptive field block (RFB) enable focus global information fires. Third, path aggregation network (PANet) neck structure bi-directional pyramid (Bi-FPN). Compared our at [email protected] by 5.1%.