Automated track monitoring has huge potential to enhance safety and reduce inspection costs. Unlike roads, train tracks have a very small turning angle or a very large turning radius to ensure passenger comfort. This makes the use of long-range narrow-FOV vision systems appropriate to inspect debris or unidentified objects on tracks that can pose a threat to the train. We conducted an experiment to assess the performance of Hammerhead with GridDetect in identifying an object of dimension 42 cm high x 45 cm wide, kept on the train tracks at various distances.
We have released three datasets, summarizing the various experiments:
They and can be visualized with NODAR viewer. Alternatively, the 3d pointclouds for the dynamic case can be accessed here: AWS S3 PC link, and we recommend using CloudCompare for visualizing them.
| Horizontal Field of View | 10 degrees |
|---|---|
| Baseline | 4.24 meters |
| Resolution | 5.4 MP |
| Bit depth | 16 bit |
| Frame rate | 2 FPS |
The cameras were mounted on two separate mounts, a truly untethered system. They were pointing toward the tracks, which were at a 50-degree angle w.r.t the principal axis of the cameras. The diagram below shows a schematic of the experimental setup.
Shown below is a topbot image captured from our cameras, showing as far as 1.5km of straight tracks.
We conducted two range tests, starting with keeping a red colored box on the tracks at 435.0m and 600.0m.
The corresponding topbot for the 435m box test is shown below