Name
Abstract | ||
|---|---|---|
Railway crossing is one of the places where mobility scooter accidents happen relatively often. To support drivers to prevent such accidents, we propose a scene recognition system for the railway crossing scene. This system can detect railway crossing scene, objects which typically exist close to the railway crossing scene, and the distance to the detected railway crossing. In this system, we propose an efficient four-stage recognition scheme that combines scene screening based on a compact CNN, CNN-based object detection, railway crossing detection, and distance estimation based on the detected warning sign of railway crossing. In the experiments, we demonstrate our system improves precision and F-score for each class by up to 20.6% and 35.0% for the same recall, respectively compared with existing object detection. Moreover, by using the proposed scene screening, we achieved 1.7 to 1.9 times faster execution for scenes in which a railway crossing does not exist on the desktop PC, Raspberry Pi3 model B, Raspberry Pi model B with Neural Compute Stick 2. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/iCAST51195.2020.9319497 | 2020 11th International Conference on Awareness Science and Technology (iCAST) |
Keywords | DocType | ISSN |
railway crossing,deep learning,image classification,object detection,decision tree | Conference | 2325-5986 |
ISBN | Citations | PageRank |
978-1-7281-9120-1 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kaisei Shimura | 1 | 0 | 0.34 |
| Yoichi Tomioka | 2 | 7 | 5.54 |
| Qiangfu Zhao | 3 | 0 | 0.34 |