26 citations · 60 across the 5 of their papers we have counts for
6 papers · 1 filter
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
Jean Pablo Vieira de Mello, Lucas Tabelini, Rodrigo F. Berriel +5
Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the mod…
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +3
Modern lane detection methods have achieved remarkable performances in complex real-world scenarios, but many have issues maintaining real-time efficiency, which is important for a…
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +4
Deep learning has been successfully applied to several problems related to autonomous driving, often relying on large databases of real target-domain images for proper training. Th…
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres +4
The reconstruction of shredded documents consists of coherently arranging fragments of paper (shreds) to recover the original document(s). A great challenge in computational recons…
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
Thiago M. Paixão, Rodrigo F. Berriel, Maria C. S. Boeres +4
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents. This task is particularly…
PolyLaneNet: Lane Estimation via Deep Polynomial Regression
Lucas Tabelini, Rodrigo Berriel, Thiago M. Paixão +3
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning. For safer self-driving vehicles, one of the problems that has y…