8 citations · 8 across the 4 of their papers we have counts for
8 papers
Ensemble-based Semi-supervised Learning to Improve Noisy Soiling Annotations in Autonomous Driving
Michal Uricar, Ganesh Sistu, Lucie Yahiaoui +1
Manual annotation of soiling on surround view cameras is a very challenging and expensive task. The unclear boundary for various soiling categories like water drops or mud particle…
Artificial Dummies for Urban Dataset Augmentation
Antonín Vobecký, David Hurych, Michal Uřičář +2
Existing datasets for training pedestrian detectors in images suffer from limited appearance and pose variation. The most challenging scenarios are rarely included because they are…
TiledSoilingNet: Tile-level Soiling Detection on Automotive Surround-view Cameras Using Coverage Metric
Arindam Das, Pavel Krizek, Ganesh Sistu +5
Automotive cameras, particularly surround-view cameras, tend to get soiled by mud, water, snow, etc. For higher levels of autonomous driving, it is necessary to have a soiling dete…
FisheyeMultiNet: Real-time Multi-task Learning Architecture for Surround-view Automated Parking System
Pullarao Maddu, Wayne Doherty, Ganesh Sistu +7
Automated Parking is a low speed manoeuvring scenario which is quite unstructured and complex, requiring full 360° near-field sensing around the vehicle. In this paper, we discuss…
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
Michal Uricar, Ganesh Sistu, Hazem Rashed +5
Wide-angle fisheye cameras are commonly used in automated driving for parking and low-speed navigation tasks. Four of such cameras form a surround-view system that provides a compl…
SoilingNet: Soiling Detection on Automotive Surround-View Cameras
Michal Uricar, Pavel Krizek, Ganesh Sistu +1
Cameras are an essential part of sensor suite in autonomous driving. Surround-view cameras are directly exposed to external environment and are vulnerable to get soiled. Cameras ha…