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Found 18 papers, 12 papers with code
Deep Learning-based Type Identification of Volumetric MRI Sequences
The analysis of Magnetic Resonance Imaging (MRI) sequences enables clinical professionals to monitor the progression of a brain tumor.
Copycat CNN: Are Random Non-Labeled Data Enough to Steal Knowledge from Black-box Models?
In a preliminary work, we presented a simple, yet powerful, method to copy black-box models by querying them with natural random images.
Deep traffic light detection by overlaying synthetic context on arbitrary natural images
By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights.
Keep your Eyes on the Lane: Real-time Attention-guided Lane Detection
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 autonomous vehicles.
Ranked #6 on
Lane Detection
on LLAMAS
What is the Best Grid-Map for Self-Driving Cars Localization? An Evaluation under Diverse Types of Illumination, Traffic, and Environment
In this work, we provide data for such analysis by comparing the accuracy of a particle filter localization when using occupancy, reflectivity, color, or semantic grid maps.
Deep Traffic Sign Detection and Recognition Without Target Domain Real Images
The method does not aim at overcoming the training with real data, but to be a compatible alternative when the real data is not available.
Self-supervised Deep Reconstruction of Mixed Strip-shredded Text Documents
The solution presented in this work extends our previous deep learning method for single-page reconstruction to a more realistic/complex scenario: the reconstruction of several mixed shredded documents at once.
PolyLaneNet: Lane Estimation via Deep Polynomial Regression
One of the main factors that contributed to the large advances in autonomous driving is the advent of deep learning.
Ranked #9 on
Lane Detection
on LLAMAS
Fast(er) Reconstruction of Shredded Text Documents via Self-Supervised Deep Asymmetric Metric Learning
The reconstruction of shredded documents consists in arranging the pieces of paper (shreds) in order to reassemble the original aspect of such documents.
Bio-Inspired Foveated Technique for Augmented-Range Vehicle Detection Using Deep Neural Networks
We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car.
Effortless Deep Training for Traffic Sign Detection Using Templates and Arbitrary Natural Images
Deep learning has been successfully applied to several problems related to autonomous driving.
Cross-Domain Car Detection Using Unsupervised Image-to-Image Translation: From Day to Night
In this work, a method for training a car detection system with annotated data from a source domain (day images) without requiring the image annotations of the target domain (night images) is presented.
Traffic Light Recognition Using Deep Learning and Prior Maps for Autonomous Cars
However, none of them combine the power of the deep learning-based detectors with prior maps to recognize the state of the relevant traffic lights.
Self-Driving Cars: A Survey
In this survey, we present the typical architecture of the autonomy system of self-driving cars.
Robotics
Memory-like Map Decay for Autonomous Vehicles based on Grid Maps
The objective of map decay is to correct invalid occupancy probabilities of map cells that are unobservable by sensors.
Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data
The copy is two-fold: i) the target network is queried with random data and its predictions are used to create a fake dataset with the knowledge of the network; and ii) a copycat network is trained with the fake dataset and should be able to achieve similar performance as the target network.
Visual Global Localization with a Hybrid WNN-CNN Approach
One of them is known as place recognition, which associates images of places with their corresponding positions.
Mapping Road Lanes Using Laser Remission and Deep Neural Networks
We propose the use of deep neural networks (DNN) for solving the problem of inferring the position and relevant properties of lanes of urban roads with poor or absent horizontal signalization, in order to allow the operation of autonomous cars in such situations.
