## 3 Improved AEGG-FD Structure
### Improved Mixed Convolutional Pyramid  
In the traditional network improvement, the addition of model convolutional layers generally improves the model accuracy, but it is difficult to achieve the real-time detection requirement with a large model. The use of the Deep Separable Convolution (DSC) module can improve the model speed because the DSC operation significantly reduces the number of model parameters and floating-point operations (FLOPs). Therefore, when the detection task has a real-time requirement, the model can utilize the DSC module in place of the original standard convolution (SC).  
However, compared to the SC, the DSC module also has some limitations. During the information transfer process, the space compression (the height and width) and channel expansion of the feature maps will cause the partial loss of semantic information. The standard convolutional computation maximally preserves the hidden connections between each pairwise channels, but the DSC operation severs these connections completely. Therefore, the DSC operation makes part of the information of the input image lost, which results in the average accuracy of the DSC module being lower than that of the SC module [46].  
In order to make the output accuracy of DSC as close to that of standard convolution as possible, this paper builds the mixed convolution (named GSConv) by mixing SC, DSC, and shuffle, which achieves improvements in both accuracy and speed (vs. the SC). When processing the input feature maps for fire detection, a DSC layer is added to the GSConv module for improving the model inference speed. At the same time, GSConv preserves the hidden connections between each channel as much as possible with low time complexity. Both the interaction of channel information and the nonlinear expression of features are enhanced in the shuffling operation, which improves the generalisation of the model. Then, the trade-off between the speed and accuracy can be achieved by GSConv. The process of GSConv is shown in Figure 8.  
On the basis of the GSConv module, inference speed and accuracy are further enhanced by the GS bottleneck and VoV module. In contrast to the original bottleneck module in the YOLOv5 model, the new bottleneck module (named GS bottleneck) utilizes the GSConv module in the main branch instead of the SC module. Meanwhile, a SC operation is added to the shortcut connection in the GS bottleneck. To further exploit the effect of GS bottlenecks, this paper designs the cross-stage partial network module (named VoV) by combining GS bottlenecks with the one-shot aggregation method. The only difference between the VoV module and the C3 module is that VoV uses the GS bottleneck. After being processed through the GSConv convolution, the feature information is stable enough to export to the head. The complexity of model computation is reduced in the VoV module by borrowing the idea of CSP. Moreover, GSConv convolution with stronger feature extraction and fusion capabilities is incorporated into the main branch of Vo  
Figure 8: The structure of the GSConv module.  
inference speed of the model can be improved. The structures of the GS bottleneck and VoV module are shown in Figure 9. In summary, the convolutional feature pyramid mixed by the two modules is combined with Path Aggregation Network (PAN) for the purpose of constructing the GSConv neck (named GSCneck).