# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 6 Conclusions and Future Work  
This paper delves into the methodology of fire smoke monitoring and optimization for tunnel engineering, leveraging a fusion of deep learning and edge computing. The central objective revolves around tackling the challenge of attaining accuracy and real-time performance for target detection models on edge devices, which results in significant enhancements to the model's architecture. Moreover, the paper accounts for the distinctive attributes of flame smoke within tunnel environments, ultimately ensuring the fulfillment of accuracy and real-time requisites on edge devices. The primary contributions and research findings of this paper are outlined as follows:  
Figure 12: Case analysis effect diagram. 1. Characteristic Engineering for Tunnel Scene Optimization: The paper initiates by examining the unique attributes of light and smoke within tunnel scenes. To address the challenges associated with smoke prediction box detection during experiments, a novel approach is introduced, which incorporates fuzzy loss to improve both the convergence speed and accuracy of the model. Acknowledging the varying illumination conditions in tunnel scenes and the image characteristics within the dataset, adjustments are made to image brightness and contrast. This serves to elevate the training complexity and reinforce the model's robustness. Furthermore, the paper explores the synergistic potential of combining wavelet transform with convolutional neural networks, leveraging the relationship between convolution and the frequency domain to enhance accuracy. 2. Model Structure Optimization: The paper advances to optimize the model structure. Significantly, the standard convolution module is substituted with a depth separable convolution component, which leads to improved model speed. Moreover, the introduction of a cross-stage partial connection layer, coupled with deep separable convolution, contributes to enhanced detection accuracy within the YOLOX-based model. The experimental results underscore the effectiveness of the approach presented in this paper, demonstrating its capacity to effectively strike a balance between the requirements for detection speed and accuracy when compared to the baseline YOLOX method. 3. Addressing Edge Computing Constraints: Acknowledging the constraints imposed by limited computing power and storage capacity on edge devices, the paper employs the channel knowledge distillation algorithm. This approach seeks to bridge the accuracy gap between the distilled model and the standard model while utilizing only 18.5% of the parameters of the standard model. Although this study implemented intelligent fire detection in tunnel environments using deep learning and edge computing, several limitations remain, which suggest potential future research directions:  
1. Model Optimization and Automated Design: The current model is manually designed, which may not be the optimal choice. With the advancement of Neural Network Architecture Search (NAS) techniques, many task-specific models have achieved superior performance on benchmark datasets such as COCO and VOC. Future research could explore NAS-based automated model search to optimize flame and smoke detection models, improving accuracy and robustness. 2. Dataset Expansion and Weakly Supervised Learning: The dataset used in this study is relatively small, containing only about 4600 images, and predominantly features smoke scenarios after a fire outbreak. As a result, the detection performance for early-stage fires, where smoke is sparse, remains suboptimal. With the development of large-scale deep learning models, particularly the Segment Anything Model (SAM), future research could explore semi-supervised or weakly supervised learning approaches as alternatives to traditional manually labeled datasets. Additionally, the vast amount of unlabeled fire and smoke images available online presents an opportunity for data augmentation through self-supervised or semi-supervised learning.