# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 1 Introduction
Images offer a wealth of information, encompassing details like location, size, and shape, rendering them highly sensitive and precise for fire detection. Consequently, video fire detection technology, grounded in computer vision and digital image processing, has witnessed substantial advancements in recent years. This technology delivers several noteworthy advantages in comparison to traditional fire detectors. It offers extensive monitoring coverage, possesses long-range capabilities, and ensures swift responses [16]. Furthermore, it operates autonomously regardless of environmental conditions such as air density, humidity, and dust levels, displaying exceptional resistance to interference and maintaining stability. In specific situations, the integration of cameras with temperature and smoke sensors can further enhance detection accuracy [17]. Video-based fire detection has found various applications and research endeavors. [PERSON] et al. [18] utilized uniform local binary patterns and discrete wavelet transforms for texture feature extraction from smoke images. These features were then input into an AdaBoost classifier for smoke image recognition and classification. However, this method encounters difficulties in quickly identifying smoke when its concentration is low. [PERSON] et al. [19] addressed this challenge by segmenting suspected smoke areas using an enhanced three-frame difference approach and subsequently applying a support vector machine for smoke detection. Nevertheless, it may fail to detect smoke in the presence of slow-moving vehicles or drifting leaves. [PERSON] et al. [20] introduced a smoke detection technique using single-frame video sequences, which is suitable for large and complex forest fire scenes. However, this method suffers from limited feature extraction, resulting in reduced accuracy. [PERSON] et al. introduced an adaptive background subtraction algorithm for detecting smoke and flames in open spaces [21], classifying moving objects. While these methods have shown promise, they have received limited attention in tunnel environments. Extracting flame and smoke characteristics in tunnels remains challenging, impacting the accuracy and real-time monitoring crucial for such scenarios. Furthermore, with the rapid advancement of edge devices and edge computing, processing data closer to the data source has emerged as a viable solution to meet real-time requirements [22; 23]. Nevertheless, few studies have focused on optimizing algorithms for edge devices, considering both accuracy and speed within the constraints of limited computing power [24; 25]. The rapid development of edge computing models provides new solutions [26]. Based on a comprehensive analysis of domestic and international research, it is evident that fire monitoring methods utilizing deep learning are viable [27]. However, research specific to tunnel environments is relatively scarce [28]. Traditional fire detection methods typically involve feature extraction based on flame and smoke color, texture, and motion characteristics, using algorithms such as SVM for classification, which often fail to meet real-time detection requirements [29; 30]. Existing methods struggle to extract flame and smoke features within tunnel scenarios, thus failing to fulfill the demands for both accuracy and real-time monitoring in such settings. Simultaneously, with the rapid development of edge devices, edge computing has emerged as an effective solution for real-time tasks. In scenarios such as defect detection [31], employing deep learning methods in edge computing has shown promising results [32]. Yet, there is limited research on optimizing algorithm efficiency within the constraints of limited computing resources at the edge. These issues warrant further in-depth investigation and exploration. To address the pressing need for intelligent, adaptive, high-precision, and real-time tunnel fire monitoring, this study investigates the efficient deployment of the YOLOX convolutional neural network in conjunction with edge computing technology.