Research on Urban Fire Detection Algorithm Based on Improved YOLOv5
The economic losses and personnel injuries caused by fires have always been a thorny issue in society,and there is an urgent need for solutions that can monitor the occurrence of fires in real-time and accurately.A modified YOLOv5 algorithm for detecting pyrotechnic targets in urban fire scenes is proposed to address the problems of complexity,small targets,and high positioning requirements in urban fire scenes.Firstly,the collected network data is organized,the dataset is constructed,and the data enhancement is performed.Then,based on the YOLOv5s algorithm model,the network structure is reconstructed and a small object detection layer is added to make the model pay more attention to small object detection.Finally,the Squeeze-and-Excitation Network(SENet)are embedded to further improve the detection accuracy of the YOLOv5 model.In addition,the issue of adding locations for SENet is also discussed.The experimental results show that the accuracy of the improved YOLOv5 algorithm has reached 93.7％,and compared with the original YOLOv5s,the recall rate and average accuracy have increased by 1.9％and 1.6％;it is found that adding the attention module at different locations in the network produces different modeling effects.
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