Abstract：A forest fire smoke detection algorithm based on improved YOLOv5s is proposed. A fire and smoke dataset containing 16573 images is constructed to solve the problem of insufficient training data sets and improve the generalization ability of the training model. A lightweight GC-C3 module is designed to replace the original C3 module and reduce the number of model parameters and calculation. The weighted bidirectional feature pyramid network is integrated into the Neck structure to enhance the detection ability of the network for small and medium targets. The network space pyramid pool structure is modified, SPPF is replaced by SimSPPF structure, and the computing efficiency and detection accuracy of the network are improved. The bounding box regression loss function CIOU is replaced by Focal-EIOU to accelerate the convergence of the model and solve the problem of mismatch between positive and negative samples. The experimental results show that the average detection accuracy of the improved network is increased by 2.3%, the number of model parameters is decreased by 46.7%, and the calculation amount of the model is decreased by 47.5%.
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2024, Vol. 45 
