Fire Detection System Based on YOLOv5 with Attention Mechanism

The increasing frequency of fire incidents in dense urban areas has highlighted the limitations of traditional sensor-based detection systems, such as delayed response and high false alarm rates. To address these challenges, this study proposes an enhanced fire detection algorithm based on You Only Look Once Version 5 (YOLOv5), incorporating a Squeeze-and-Excitation (SE) attention mechanism. The SE module was selected for its effective channel-wise feature recalibration capability and its minimal computational overhead, making it ideal for deployment on edge devices in real-time scenarios. Experimental results demonstrate that the proposed method achieves a precision of 0.87, recall of 0.86, and mAP@0.5 of 0.91, with a mAP@0.5:0.95 of 0.42, indicating robust performance under stricter evaluation criteria. The model maintains high computational efficiency, exhibiting strong potential for practical, real-time fire detection applications.