Banca de DEFESA: NELSON ALVES FERREIRA NETO

Uma banca de DEFESA de DOUTORADO foi cadastrada pelo programa.
STUDENT : NELSON ALVES FERREIRA NETO
DATE: 11/07/2022
TIME: 09:00
LOCAL: https://conferenciaweb.rnp.br/webconf/laboratorio-de-sistemas-digitais-ufba
TITLE:

Perception for Autonomous Vehicles in Off-Road Environment Using Deep Learning

KEY WORDS:

Perception, Deep learning, Artificial Neural Network, Autonomous Car, Autonomous
Vehicle, Convolutional Neural Network, AI, CNN, ADAS, Real-time Segmentation, Off-Road
Segmentation.

PAGES: 121
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

Robust systems are required for autonomous driving on non-uniform terrain commonly found
in open-pit mines and developing countries. To help narrow the gap in this kind of application,
this work proposes a perception system for autonomous vehicles and advanced driver assistance
specialized on unpaved roads and off-road environments capable of navigating through rough
terrain without a predefined trail. As part of this system, the Configurable Modular Segmentation
Network (CMSNet) framework is proposed facilitating the creation of different architectures
arrangements. Some CMSNet configurations were ported and trained to segment obstacles and
trafficable ground on a new collection of images from unpaved roads and off-road scenarios
containing adverse conditions such as night, rain, and dust. It was also performed: an investiga-
tion regarding the feasibility of applying deep learning to detect regions where the vehicle can
pass through when there is no clear track boundary; a study of how our proposed segmentation
algorithms behave in different severity levels of visibility impairment; and an evaluation of field
tests carried out with semantic segmentation architectures conditioned for real-time inference.
The new dataset (named Kamino) has almost 12,000 new images collected from an operated
vehicle with various sensors, including eight cameras capturing synchronized sequences from
different points of view. The Kamino dataset has a high number of labeled pixels compared to
similar publicly available collections. It includes images collected from an off-road proving
ground exclusively assembled for testing the system that emulates an open-pit mine scenario
under different adverse conditions of visibility. To achieve embedded real-time inference and
allows field tests, many layers of the CMSNet CNN networks were methodically removed and
fused using TensorRT, C++, and CUDA. Empirical experiments on two datasets validated the
effectiveness of the proposed system.

BANKING MEMBERS:
Interno - 1856678 - PAULO CESAR MACHADO DE ABREU FARIAS
Interno - 2506534 - EDUARDO FURTADO DE SIMAS FILHO
Interno - 1938914 - TIAGO TRINDADE RIBEIRO
Interno - 2042176 - WAGNER LUIZ ALVES DE OLIVEIRA
Externo à Instituição - RICARDO MENEZES PRATES - UNIVASF
Externo à Instituição - JOAO PAULO PAPA - UNESP
Externo à Instituição - GUILHERME DE ALENCAR BARRETO - UFC