As the rapid development of the rail transportation industry, rail transportation becomes more popular as a component of urban public transport systems, but the fallen obstacle(s) from the rail platform becomes the terrible hidden danger for the rail transportation. As an enclosed public transport systems, rail transportation creates gathered crowd both on board and on the platform. Although railway is the safest form of land transportation, it is capable of producing lots of casualties, when there is an accident.
There are several conventional systems of obstacles detection in platform monitoring systems like stereo visions, thermal scanning, and vision metric scanning, etc. As the traditional detection systems could not achieve the demand of detecting the obstacles on the rail within the platform.
In this thesis, the author designs a system within the platform based on laser sensors, virtual instruments technology, and image processing technology (machine vision) to increase the efficiency of detection system. The system is useful for guarantying the safety of rail vehicle when coming into the platform and avoid obstacle(s) on the rail fallen from the platform, having a positive impact on traffic safety to protect lives of people.
The author used LabVIEW software to create a simulation environment where the input blocks represent the functionalities of the system, in which simulated train detection and fallen object detection. In this thesis, the author mainly focuses on fallen object detection. For fallen object detection, the author used 2D image processing method to detect obstacle(s), so the function is, before the rail vehicle comes into the platform, the system could detect whether there is fallen obstacle(s) on the rail within the platform, simultaneously categorize size of the obstacle(s), and then alarm for delivering the results.
Source: University of Gävle
Author: Tang, Shengjie