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.
关键词
火灾烟雾检测/YOLOv5s/注意力机制/神经网络
Key words
fire smoke detection/YOLOv5s/attention mechanism/neural network
维普
万方数据