A Forest Fire Detection Method Based on Re-parameterized YOLOv5s
Currently,forest fires are frequent.It is particularly important to establish daily monitoring.However,the low computational power and memory of edge intelligent detection device limit the reasoning and deployment of detection model.To address the above issues,an improved forest fire detection algorithm based on re-parameterized YOLOv5s is proposed,which combines lightweight ideas such as re-parameterization,channel rearrangement,and Depthwise Separable Convolution(DSC)to design new backbone and neck networks respectively,enhancing feature extraction capabilities,improving model detection accuracy,and significantly reducing the amount of parameters and reasoning weight.To avoid information loss in the neck network,a feature enhancement module is proposed based on hole convolution to enhance the multi-scale feature fusion ability.In order to further improve the performance of the model,a lightweight CA attention mechanism is added to more accurately locate the target.In addition,the currently published flame and smoke data sets have a problem of being not targeted.In order to better improve the detection efficiency of the model,a new forest fire data set has been created.At the same time,structural similarity algorithms have been used to eliminate images with high similarity on the data set,ensuring the generalization ability of the model.Experimental results show that improved re-parameterized YOLOv5s improves the accuracy by 4.0％with about 76％of the original network's parameter amount,while reducing the inference weight to 10.5 MB,making it more suitable for edge devices with poor equipment performance and small capacity,and improving the efficiency of forest fire patrol inspection.
万方数据
维普
