Research on fire smoke scene detection algorithm based on improved YOLOv5s
A fire smoke detection algorithm based on YOLOv5s is proposed to solve the problems of insufficient accuracy and insufficient feature expression ability of small objects detection in early fire smoke in various environments.Firstly,a detailed analysis of the structure of the fire smoke image dataset is conducted.Then,according to the characteristics of the dataset structure,channel attention mechanism and spatial attention mechanism are introduced into the backbone network.Finally,a small object detection layer is added to pay more attention to the detection of small objects.The experimental results show that the accuracy of the improved YOLOv5s algorithm reaches 89.9%,and its average accuracy PmA is increased by 5.0%compared with the original YOLOv5s.The model shows excellent results in fire smoke detection and has guiding significance for early warning of fire smoke.
fire smoke detectionYOLOv5sattention mechanismneural network
万方数据
维普
