Improved YOLOv5 lightweight grassland smoke detection algorithm

To address the problems of low detection performance and large memory consumption of traditional smoke and fire detection methods in complex scenes such as grasslands. Based on the YOLOv5 model, a YOLOv5-GDE optimization model is proposed. The C3 module in YOLOv5 is replaced by GhostC3 with a smaller number of parameters, and some standard convolution blocks are replaced by depth-separable convolutions to make the model more lightweight. Finally, to solve the problem of unstable target regression frame, the EIoU loss function is introduced, which effectively improves the convergence speed and detection accuracy of the model. Experimental results on the homemade grassland smoke dataset show that the optimized model reduces the number of parameters and computational effort by 65.4% and 65.8%, respectively, compared with the original model, and the model size is only 36.8% of the original model, which is more suitable for smoke target detection in grassland scenes and more suitable for deployment in embedded devices with limited computational power, under the premise of ensuring detection accuracy.