TY - CHAP
T1 - Multi-objective Evaluation of Deep Learning Based Semantic Segmentation for Autonomous Driving Systems
AU - Olvera, Cynthia
AU - Rubio, Yoshio
AU - Montiel, Oscar
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Recent applications of deep learning (DL) architectures for semantic segmentation had led to a significant development in autonomous driving systems (ADS). Most of the semantic segmentation applications for ADS consider a plethora of classes. Nevertheless, we believe that focusing only on the segmentation of drivable roads, sidewalks, traffic signs, and cars, can drive the improvement of navigation and control techniques in autonomous vehicles. In this study, some state-of-the-art topologies are analyzed to find a strategy that can achieve a uniform performance for the four classes. We propose a multiple objective evaluation method with the purpose of finding the non-dominated solutions in different DL architectures. Numerical results are shown using CityScapes, SYNTHIA, and CamVid datasets.
AB - Recent applications of deep learning (DL) architectures for semantic segmentation had led to a significant development in autonomous driving systems (ADS). Most of the semantic segmentation applications for ADS consider a plethora of classes. Nevertheless, we believe that focusing only on the segmentation of drivable roads, sidewalks, traffic signs, and cars, can drive the improvement of navigation and control techniques in autonomous vehicles. In this study, some state-of-the-art topologies are analyzed to find a strategy that can achieve a uniform performance for the four classes. We propose a multiple objective evaluation method with the purpose of finding the non-dominated solutions in different DL architectures. Numerical results are shown using CityScapes, SYNTHIA, and CamVid datasets.
KW - Autonomous vehicles
KW - Deep learning
KW - Road detection
KW - Semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85080950887&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-35445-9_23
DO - 10.1007/978-3-030-35445-9_23
M3 - Capítulo
AN - SCOPUS:85080950887
T3 - Studies in Computational Intelligence
SP - 299
EP - 311
BT - Studies in Computational Intelligence
PB - Springer
ER -