Drivable Road Area Detection with Regression Output CNN
| Autor: | Onur Acun, Ayhan Kucukmanisa, Yakup Genc, Oguzhan Urhan |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
050210 logistics & transportation
Semantics (computer science) business.industry Computer science Deep learning 05 social sciences Boundary (topology) Advanced driver assistance systems 02 engineering and technology Image segmentation computer.software_genre Regression 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing The Internet Artificial intelligence Data mining business computer |
| Zdroj: | SIU |
| DOI: | 10.1109/siu49456.2020.9302116 |
| Popis: | Nowadays, many methods are developed on autonomous vehicles and driver assistance systems to prevent traffic accidents and support drivers. In this work, a drivable area detection method based on CNN and regression is proposed. In the proposed method, Cityscapes dataset, which is open to sharing on the Internet is used as dataset. The images in the dataset are cut into slices to obtain new input images. With those images, a CNN based deep learning network is trained. By applying linear regression on the characteristics of the output of the network, the road boundary points in the relevant slice are tried to be determined. Experimental results have shown that the developed method has real-time operating performance and the results can be improved. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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