An Early Warning System of Tram Safety Protection Based on Multiinformation Detection Aiming at the existing collision accidents with motor vehicles and pedestrians in the operation of tram, a tram safety protection early warning system is proposed, which can automatically analyze and interpret dangerous objects in the direction of moving forward. The system uses ranging sensor (Ultrasonic, Camera, Ranging radar) and infrared video information to obtain the accurate distance between the obstacle and the locomotive. The obstacle type and potential running speed can be obtained by infrared image recognition and classification. According to the direction of the tram, the distance, type and speed of the obstacles, the safety level of the obstacles can be determined. The above analysis and interpretation results are displayed intuitively on the onboard display and reminder unit. Relevant technologies can effectively improve the ability of the tram driver to obtain information, automatically detect obstacles and judge the degree of danger, and improve the safety of the tram.

The enhancement of catenary image with low visibility based on multifeature fusion network in railway industry Abstract   In the Industrial Internet of Things (IIoT), the security and efficiency are indispensable. For the railway industry, the video from inspection vehicle would be influenced by various factors with low visibility and hard for high level vision task, such as the fault diagnosis of catenary system. In this paper, we propose a method based on the multifeature fusion network to improve the quality and visual effect of the catenary images. The transmission map is learned from the multiscale and multifeature fusion network, which would learn coarse and fine details and combine the latent features. In the catenary image, the sky and nonsky regions are segmented through multiple accommodative thresholds to estimate the atmospheric light value. With the refinement of transmission map, the restored catenary image is obtained through the atmospheric scattering model. In the experimental results, it can be seen that the proposed method can improve the clarity of catenary image in haze. The quantitative evaluation shows that it has better visual effect compared with the other traditional methods.