MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic Applications

Alonso, Íñigo (Universidad de Zaragoza) ; Riazuelo, Luis (Universidad de Zaragoza) ; Murillo, Ana C. (Universidad de Zaragoza)

Resumen: Efficient models for semantic segmentation, in terms of memory, speed, and computation, could boost many robotic applications with strong computational and temporal restrictions. This article presents a detailed analysis of different techniques for efficient semantic segmentation. Following this analysis, we have developed a novel architecture, MiniNet-v2, an enhanced version of MiniNet. MiniNet-v2 is built considering the best option depending on CPU or GPU availability. It reaches comparable accuracy to the state-of-the-art models but uses less memory and computational resources. We validate and analyze the details of our architecture through a comprehensive set of experiments on public benchmarks (Cityscapes, Camvid, and COCO-Text datasets), showing its benefits over relevant prior work. Our experiments include a sample application where these models can boost existing robotic applications.

Idioma: Inglés

DOI: 10.1109/TRO.2020.2974099

Año: 2020

Publicado en: IEEE Transactions on Robotics 36, 4 (2020), 1340 - 1347
ISSN: 1552-3098

Factor impacto JCR: 5.567 (2020)

Categ. JCR: ROBOTICS rank: 4 / 28 = 0.143 (2020) - Q1 - T1

Factor impacto SCIMAGO: 2.027 - Computer Science Applications (Q1) - Electrical and Electronic Engineering (Q1) - Control and Systems Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/T45-17R
Financiación: info:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7
Financiación: info:eu-repo/grantAgreement/ES/MICIU-FEDER/PGC2018-098817-A-I00
Financiación: info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/DPI2016-76676-R

Tipo y forma: Artículo (PostPrint)

Área (Departamento): Área Ingen.Sistemas y Automát. (Dpto. Informát.Ingenie.Sistms.)