Abstract: In order to improve the safety and efficiency of tunnel operation, a multi-sensor based tunnel operation risk perception method is proposed. Firstly, an object detection method based on YOLOv7(You Only Look Once v7) is used to effectively detect information such as traffic flow and speed from video sensors, and a deep temporal convolutional network algorithm is used to dynamically evaluate tunnel operation risks. Video sensors are integrated with multiple sensing devices such as smoke and temperature sensors to form a fire risk monitoring system, and multiple sensing devices are integrated to form a fire monitoring system. By numerically modeling
the fire monitoring status under different combustion conditions inside the tunnel, the distribution and development patterns of temperature, toxic gas concentration, and smoke inside the tunnel are analyzed, providing a risk assessment of tunnel fires. This method is applied in some tunnels of Shandong Jiwei Expressway to ensure the safety of the tunnels.

Key words: multiple sensors, tunnel target detection, temperature detection, gas concentration detection, visibility detection