基于改进YOLOv8的城市火灾检测算法OA

In view of the fact that the traditional fire detection algorithm has low detection accuracy and high false detection rate in complex urban backgrounds,a city fire detection algorithm based on improved YOLOv8 is proposed.Firstly,based on the YOLOv8 object detection model,within the neck network,the Bi-directional Feature Pyramid Network structure is introduced to replace the Path Aggregation Network-Feature Pyramid Network feature fusion layer,fusing multi-scale feature information and enhancing the model's feature learning ability.Secondly,the Efficient Multi Scale Attention mechanism is integrated into the BiFPN to improve the network's feature extraction capability and further enhance the accuracy of smoke and fire detection.Finally,the partial convolution module is introduced into the backbone network to replace the C2f module with the C2f-Faster module,improving the detection efficiency of the model and reducing redundant calculations.The improved algorithm is applied to a self-compiled dataset of smoke and fire for experimentation.The result demonstrates that the improved model achieved a mAP@50 of 73.6%compared to the original model,reduced the number of parameters by 8.99%,and reduced the computational complexity to 7.7 GFLOPs.While enhancing the detection accuracy,the model has been lightweight.The improved model can meet the requirements of smoke and fire detection in complex urban backgrounds.