Research on a Smoke and Fire Detection System for Subway Station Scenes Based on YOLOv5
In view of the lack of intelligence in the current security monitoring of subway stations,a smoke and fire detection system based on YOLOv5 is proposed to improve the efficiency of fire detection.The system uses YOLOv5 algorithm to detect the abnormal situation and supports the dynamic detection of smoke and flame.Multi-channel video parallel reasoning monitoring can improve the monitoring effici-ency of the system.CBAM attention mechanism is added to the backbone network,and based on the dynamic characteristics of the flame,the false detection suppression algorithm ODF-LOOK based on frame difference method is proposed to reduce the influence of static false detection on the output results.The experimental results show that the recognition accuracy of the improved algorithm is improved to 98.9％.and the functions of voice alarm and cloud short message alarm work fine,which is effective and convenient for detecting abnormal smoke and fire.The system has high practical significance in preventing fire accidents and im-proving safety level.
Target detectionYOLOv5Smoke and fire detectionFrame differences methodRail traffic
万方数据
维普
