Lab

INVETT Research Group

Featured research (2)

Road detection and segmentation is a crucial task in computer vision for safe autonomous driving. With this in mind, a new net architecture (3D-DEEP) and its end-to-end training methodology for CNN-based semantic segmentation are described along this paper for. The method relies on disparity filtered and LiDAR projected images for three-dimensional information and image feature extraction through fully convolutional networks architectures. The developed models were trained and validated over Cityscapes dataset using just fine annotation examples with 19 different training classes, and over KITTI road dataset. 72.32% mean intersection over union(mIoU) has been obtained for the 19 Cityscapes training classes using the validation images. On the other hand, over KITTIdataset the model has achieved an F1 error value of 97.85% invalidation and 96.02% using the test images.

Lab head

David Fernández-Llorca
Department
  • Computer Engineering Department

Members (10)

Miguel-Angel Sotelo
  • University of Alcalá
Noelia Hernández
  • University of Alcalá
Ruben Izquierdo
  • University of Alcalá
Ivan garcia daza
  • University of Alcalá
Álvaro Quintanar Pascual
  • University of Alcalá
Javier Lorenzo Díaz
  • University of Alcalá
Héctor Corrales Sánchez
  • University of Alcalá
Antonio Hernandez Martinez
  • University of Alcalá
I. Parra
I. Parra
  • Not confirmed yet