Name
Title | ||
|---|---|---|
Automating Inspection of Moveable Lane Barrier for Auckland Harbour Bridge Traffic Safety. |
Abstract | ||
|---|---|---|
A moveable lane barrier along the Auckland Harbour Bridge (AHB) enables two-way traffic flow optimisation and control. However, the AHB barrier transfer machines are not equipped with an automated solution for screening of the pins that link the barrier segments. To improve traffic safety, the aim of this paper is to combine traditional machine with deep learning approaches to aid visual pin inspection. For model training with imbalanced dataset, we have included additional synthetic frames depicting unsafe pin positions produced from collected videos. Preliminary experiments on produced models indicate that we are able to identify unsafe pin positions with precision and recall up to 0.995. To improve traffic safety beyond the AHB case study, future developments will include extended datasets to produce near-real time IoT alerting solutions using mobile and other video sources. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/978-3-030-63830-6_13 | ICONIP (1) |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Boris Bacic | 1 | 10 | 3.23 |
| Munish Rathee | 2 | 0 | 0.34 |
| Russel Pears | 3 | 205 | 27.00 |