# Intelligent Monitoring of Tunnel Fire Smoke Based on Improved YOLOX and Edge Computing
## 1 Introduction  
China's infrastructure witnessed remarkable growth in the 21 st century, as revealed in the 2020 statistical bulletin from the Ministry of Transportation. By the end of 2020, the national highway network had expanded to an impressive 5.1981 million kilometers, reflecting an increase of 0.1856 million kilometers compared to the previous year [1]. Within this extensive highway network, there were 21,316 tunnels, encompassing a total length of 21.9993 million meters. This marked an increase of 2249 tunnels and 3.0327 million meters in length compared to the preceding year. Tunnels, by their nature, pose unique challenges due to their confined spaces and limited visibility. Upon entering a tunnel, drivers are confronted with issues such as reduced visibility attributed to the \"Black Frame\" effect, the \"Black Hole\" effect, and the restricting impact of tunnel walls [2, 3, 4]. These factors significantly elevate the likelihood of accidents, including vehicle collisions and unexpected stops, within the tunnel environment. The occurrence of tunnel fire incidents can be attributed to various factors, including electrical circuit or equipment short circuits, combustion from car carburetors, human arson, smoking, and acts of terrorism, among others [5, 6]. Tunnel fires, especially in long and super-long tunnels, pose a severe threat. Once a fire breaks out, it can lead to a series of critical issues [7]. Firstly, the rapid spread of flames within the tunnel's confined structure leaves little time for the evacuation of individuals and vehicles, increasing the risk of casualties [8]. Secondly, the production oftoxic smoke and gases from the combustion, if not promptly ventilated, results in a sharp deterioration of the tunnel's air quality, jeopardizing the safety of those inside [9]. If external firefighting and rescue personnel cannot effectively organize firefighting and rescue efforts, the consequences can be devastating, including suffocation, burns, poisoning, and even fatalities. Additionally, extensive damage to tunnel facilities may lead to prolonged road closures, causing inestimable economic losses [10]. Therefore, early fire detection within tunnels is crucial for ensuring their safe operation, and it is an urgent issue that tunnel security departments need to address. Fire detection in tunnel environments differs significantly from other open or semi-open spaces due to environmental conditions, smoke propagation characteristics, visual interference factors, and real-time detection requirements. First, airflow in tunnels is restricted, causing smoke to spread rapidly along the ventilation direction. This makes traditional smoke morphology and diffusion-based detection methods less effective [11]. Second, complex and uneven lighting conditions in tunnels, especially due to vehicle entry and exit or light reflections, can lead to sensor or camera misdetections [12]. Additionally, the background in tunnels is highly complex, with high temperature and humidity levels, which can interfere with conventional flame color or motion-based detection methods, reducing detection accuracy [13]. Finally, since tunnels are confined spaces, fire incidents pose greater challenges for personnel evacuation and emergency response, necessitating higher real-time detection accuracy and robustness [12]. Historically, early fire detection has depended on various sensors that analyze factors such as transparency, spectrum, temperature, and smoke concentration within a designated area to determine the presence of a fire. Regrettably, these sensors suffer from certain limitations. They exhibit sluggish responses to atmospheric changes, demonstrate poor stability, and struggle to promptly identify fire signals due to factors such as airflow patterns, detection range, thermal barriers, and other considerations [14; 15]. In contrast, the utilization of imagery provides distinct advantages.