## 5 Conclusions  
In this paper, a new fire detection model, AEGG-FD, is proposed to balance speed, accuracy, and complexity in dealing with fire detection tasks. The method proposed in this paper consists of four main parts: reconstruction of the backbone, improvement of the neck, reformulation of the loss function, and design of the flame-centre detection function. Specifically, the reconstructed of the backbone means that the backbone network for extracting features is rebuilt using the Ghost module, which makes the model more lightweight, and the SECSP attention mechanism, which emphasizes the channel information, is added to enhance the ability of extracting the fire feature information. In the second group of experiments in Section 4.5, it can be seen that the reconstructed backbone results in more compact and lower-complexity model structures. The improvement of the neck means improving the feature pyramid at the neck by replacing the standard convolution (SC) with the mixed convolution GSConv, while integrating a cross-stage partial network module that incorporates the GS bottleneck to form the GSCneck. Model 4 and Models 8 to 10 shown in Table 7 demonstrate that GSCneck accomplishes the task of improving the model detection speed and maintaining accuracy. The loss function SIoU increases the sensitivity of the model towards the channel features. From the experimental results, it can be seen that using the SIoU improves the inference ability and accuracy of the model. Finally, the flame-centre detection (FD) module that extracts the information of the flame-centre region is designed to achieve the real-time localization of the fire region, which is shown in the comparative experiments of Section 4.6 that the FD module can further improve the detection accuracy and efficiency of the model. In summary, our model achieves a superior balance between speed, accuracy, and complexity, which outperforms the original network in fire detection tasks. From the results of ablation experiments, it can be seen that the model proposed in this paper can achieve 84.7% mAP\({}_{0.5}\) and 60.2 FPS, while the model parameters are only 5.24 M.  
Currently, many research focuses on developing multimodal studies of fire detection for other complicated problems such as merging smoke features or combining sensors. Therefore, how to effectively extend the currently proposed AEGG-FD for solving these problems is an important future work. Conceptualization, J.L. and J.Y. ; methodology, J.L. and J.Y. ; software, J.Y. ; validation, J.Y. ; formal analysis, J.Y. ; investigation, J.Y. and Z.Y. ; resources, Z.Y. ; data curation, J.Y. ; writing--original draft preparation, J.L., J.Y. and Z.Y. ; writing--review and editing, J.L., J.Y. and Z.Y. ; visualization, J.Y. ; supervision, J.L. and Z.Y. ; project administration, J.L. ; funding acquisition, J.L. and Z.Y. All authors have read and agreed to the published version of the manuscript. Figure 21: Positioning effect on the flame-centre area after using the FD module. **Funding:** This research was supported by the Development of Multi-Source Micro-grid: Intelligent Control, Efficient Thermal Management, Noise Reduction, and Infrared Stealth Technology (grant number 20223 AAE02012); the Key Technology Research on High-Power Hydrogen Fuel Cell Metal Ultra-Thin Bipolar Plates for Multi-Source Energy Equipment (grant number 20232 BCJ22058); the Young Talent Cultivation Innovation Fund Project of Nanchang University (grant number 9167-28740080); and the Topology optimization design of multi-scale composite porous metamaterials (grant number BSKYCXZX 2023-07). **Informed Consent Statement:** Not applicable. **Data Availability Statement:** The data presented in this study are available on request from the corresponding author. **Conflicts of Interest:** Author [PERSON] was employed by the company Jiangxi Tellhow Military Industry Group Co., Ltd.