Name
Abstract | ||
|---|---|---|
Semantic segmentation (SS) partitions an image into several coherent semantically meaningful parts and classifies each part into one of the pre-determined classes. In this paper, we argue that the existing SS methods cannot be reliably applied to autonomous driving system as they ignore the different importance levels of distinct classes for safe driving. For example, pedestrian, car, and bicyclis... |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TITS.2018.2801309 | IEEE Transactions on Intelligent Transportation Systems |
Keywords | Field | DocType |
Image segmentation,Autonomous vehicles,Roads,Neural networks,Feature extraction,Semantics,Reliability | Computer vision,Pedestrian,Data set,Backward propagation,Segmentation,Artificial intelligence,Deep learning,Engineering,Deep neural networks,Machine learning | Journal |
Volume | Issue | ISSN |
20 | 1 | 1524-9050 |
Citations | PageRank | References |
5 | 0.41 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bi-ke Chen | 1 | 5 | 0.41 |
| Chen Gong | 2 | 37 | 6.76 |
| Jian Yang | 3 | 6102 | 339.77 |