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.

中文翻译：

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MiniNet：用于实时机器人应用的高效语义分割 ConvNet

在内存、速度和计算方面，语义分割的有效模型可以促进许多具有强大计算和时间限制的机器人应用程序。本文详细分析了用于有效语义分割的不同技术。根据这一分析，我们开发了一种新颖的架构 MiniNet-v2，它是 MiniNet 的增强版本。MiniNet-v2 是根据 CPU 或 GPU 可用性考虑最佳选项而构建的。它达到了与最先进模型相当的准确性，但使用的内存和计算资源更少。我们通过对公共基准（Cityscapes、Camvid 和 COCO-Text 数据集）的一组综合实验来验证和分析我们架构的细节，显示其优于相关先前工作的优势。