doi:10.1109/TRO.2020.2974099engAlonso, ÍñigoRiazuelo, LuisMurillo, Ana C.MiniNet: An Efficient Semantic Segmentation ConvNet for Real-Time Robotic ApplicationsART-2020-117975Efficient 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.2020http://zaguan.unizar.es/record/9944610.1109/TRO.2020.2974099http://zaguan.unizar.es/record/99446oai:zaguan.unizar.es:99446info:eu-repo/grantAgreement/ES/DGA/T45-17Rinfo:eu-repo/grantAgreement/ES/MCIU-AEI/RTC-2017-6421-7info:eu-repo/grantAgreement/ES/MICIU-FEDER/PGC2018-098817-A-I00info:eu-repo/grantAgreement/ES/MINECO-AEI-FEDER/DPI2016-76676-RIEEE Transactions on Robotics 36, 4 (2020), 1340 - 1347All rights reservedhttp://www.europeana.eu/rights/rr-f/info:eu-repo/semantics/openAccess