Application of wireless data collection driven by intelligent sensors in bridge collision warning

Abstract

To address the challenges of ship deviation and bridge collision prevention in the water transportation industry, as well as to minimize missed alarms related to these incidents, the study develops an intelligent bridge collision and deviation warning system based on the analysis of ship communication characteristics. The system integrates radar and cameras for capturing and tracking ship navigation video images, followed by real-time communication feature analysis of the collected data. These results are transmitted via radio frequency signals to enable a real-time hull detection mechanism using ship detection algorithms. The system evaluates the ship's safety based on preset bridge warning positions and triggers sound and light alarms in case of superelevation or yaw events to prevent collisions and ensure safe navigation. Experimental results demonstrate that the system achieves a maximum missed alarm rate of only 6 %, with warning times under 1 ms across various environmental conditions. It provides precise warnings for ships within a range of approximately 300 m, maintaining an error margin of less than 1 m. The system showcases significant potential for practical applications and widespread adoption.