| Title | Automated Video Inspection System for Grade Crossing Safety [Research Results] |
|---|---|
| Record ID | 80528 |
| Personal Name Creator |
Baillargeon, Jay; Doran, Joshua |
| Corporate Creator | United States. Department of Transportation. Federal Railroad Administration; VisioStack Inc. |
| Publisher | United States. Department of Transportation. Federal Railroad Administration |
| Publication Date | 20210401 |
| Language | English |
| Abstract | The Federal Railroad Administration (FRA) has sponsored several programs to help reduce incidents at highway-rail crossings and improve the safety of both vehicles and pedestrians. One such recent project developed an artificial intelligence (AI)-based machine vision system to help inspect highway-rail grade crossings with forward-facing video (FFV) obtained from a Class I geometry car. The goal of this research is to apply the same concepts for locomotive-mounted FFV cameras, currently in common use within the industry; it complements manual inspections. FRA supported this research through its Phase I Small Business Innovation Research (SBIR) initiative. |
| Rosap ID | dot:55545 |
| Rosap URL | https://rosap.ntl.bts.gov/view/dot/55545 |
| TRT Terms | Artificial intelligence; Video; Grade crossing protection systems; Video cameras; Detection and identification technologies |
| General Subjects | RailLinks; forward-facing video; grade crossing safety |
| Geographical Coverage |
United States |
| TRIS Online Accession No |
1770413 |
| Contract Number | 6913G620P800086 |
| Report Number | RR 21-05 |
| Resource type | Brief |
| URL | https://ntlrepository.blob.core.windows.net/lib/80000/80500/80528/AVIS-A.pdf |
| Format | |
| Database | NTL Digital Repository |