Fire Detection of yolov8 Model based on Integrated SE Attention Mechanism

Abstract

Our proposed method utilizes YOLOv8 and SE attention mechanism for detecting fires, which is crucial for early detection and prevention. Our method balances accuracy and real-time performance while detecting fires in various scenarios. The proposed method achieves an average mAP0.5 value of 0.730, with an improvement from the original model's mAP0.5 value of 0.707 after incorporating the SE attention mechanism. We evaluated our model on a benchmark dataset and demonstrated its effectiveness in accurately detecting and localizing fires with high precision and recall rates. Experimental results confirm the effectiveness of our proposed method in accurately detecting and localizing fires, demonstrating its potential for wide application and promotion in the fire safety industry.