Drivable Road Area Detection with Regression Output CNN Surulebilir Yol Alanlar1n1n Regresyon 1kil1 KSA ile Tespiti

Acun O., KÜÇÜKMANİSA A., Genc Y., URHAN O.

28th Signal Processing and Communications Applications Conference, SIU 2020, Gaziantep, Türkiye, 5 - 07 Ekim 2020 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası:
  • Doi Numarası: 10.1109/siu49456.2020.9302116
  • Basıldığı Şehir: Gaziantep
  • Basıldığı Ülke: Türkiye
  • Anahtar Kelimeler: CNN, deep learning, drivable area detection, regression
  • Kocaeli Üniversitesi Adresli: Evet

Özet

© 2020 IEEE.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.