# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 4 Model Deployment of Edge Computing Based on Knowledge Distillation  
### Knowledge Distillation  
Knowledge distillation is a technique in deep learning aimed at imparting knowledge from complex models to simpler ones [45]. In recent years, deep neural networks have achieved significant success in both industry and academia, particularly in the field of computer vision [46; 47]. The success of deep learning can be largely attributed to its scalability, enabling the handling of large-scale data and models with billions of parameters [48]. However, deploying these complex deep models on resource-constrained devices, such as mobile phones and embedded systems, presents challenges. This is due not only to high computational complexity but also the need for significant storage space [26; 49]. To address this issue, researchers have introduced various model compression and acceleration techniques, with knowledge distillation being a representative approach [50]. It effectively transfers knowledge from a large teacher model to a smaller student model [51; 52]. The core concept of knowledge distillation involves using the teacher model's outputs as targets for the student model and encouraging the student model to mimic the behavior of the teacher model, thereby achieving similar performance and generalization [53]. Knowledge distillation systems typically encompass key components such as the type of knowledge, distillation algorithms, and the architecture of teacher-student models [50]. The general framework of this approach is illustrated in Figure 7. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Module** & **Parameters** & **Flops** \\ \hline Model & 52.316 M & 80.121 G \\ Backbone & 26.139 M & 34.22 G \\ backbone.stem & 28.768 K & 2.946 G \\ backbone.stage1 & 0.253 M & 6.047 G \\ backbone.stage2 & 1.5 M & 9.182 G \\ backbone.stage3 & 5.949 M & 9.100 G \\ backbone.stage4 & 18.408 M & 6.945 G \\ Neck & 23.747 M & 25.885 G \\ neck.reduce-layers.2 & 0.525 M & 0.210 G \\ neck.top-down-layers & 3.611 M & 9.143 G \\ neck.downsample-layers & 2.951 M & 1.889 G \\ neck.bottom-up-layers & 12.529 M & 8.031 G \\ neck.out-layers & 4.13 M & 6.610 G \\ neck.upsample-layers & & 2.458 M \\ Head-module & 2.43 M & 20.016 G \\ head-module.cls-convs & 1.183 M & 9.918 G \\ head-module.reg-convs & 1.183 M & 9.918 G \\ head-module.cls & 61.68 K & 0.172 G \\ head-module.reg & 3.084 K & 8.602 M \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The overall structure of the model. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Training Epochs** & **Map** \\ \hline YOLOv5-1 & 300 & 82.1\% \\ YOLOX-1 & 300 & 83.2\% \\ Improved YOLOX & 300 & 85.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model accuracy. [PERSON] et al. introduced an innovative knowledge distillation approach tailored for dense prediction tasks such as semantic segmentation and object detection [54]. This method, known as channel knowledge distillation (CKD), places particular emphasis on the channel-level correlation between the features learned by the teacher model and those learned by the student model. CKD transfers knowledge from the teacher model by aligning the correlation among the feature map channels of both models. During the distillation process, greater emphasis is given to the most crucial areas within each channel [55]. Probability-based distance metrics, such as Kullback-Leibler divergence, are utilized to measure the dissimilarity between the feature maps of the teacher and student models [56]. This approach contributes to the capture of finer-grained details in predictions, thereby enhancing the performance of the student model [53; 55]. To evaluate the effectiveness of CKD model compression, we conducted experiments using the standard model described in Section 2.2.