Semantic Scene Segmentation for Robotics Applications

@article{Tzelepi2021SemanticSS,
  title={Semantic Scene Segmentation for Robotics Applications},
  author={Maria Tzelepi and Anastasios Tefas},
  journal={2021 12th International Conference on Information, Intelligence, Systems \& Applications (IISA)},
  year={2021},
  pages={1-4}
}
  • Maria TzelepiA. Tefas
  • Published 12 July 2021
  • Computer Science
  • 2021 12th International Conference on Information, Intelligence, Systems & Applications (IISA)
Semantic scene segmentation plays a critical role in a wide range of robotics applications, e.g., autonomous navigation. These applications are accompanied by specific computational restrictions, e.g., operation on low-power GPUs, at sufficient speed, and also for high-resolution input. Existing state-of-the-art segmentation models provide evaluation results under different setups and mainly considering high-power GPUs. In this paper, we investigate the behavior of the most successful semantic… 
[PDF] Semantic Reader
3 Citations
Highly Influential Citations
1
Background Citations
3
Methods Citations
2

Figures and Tables from this paper

3 Citations

Automatic Generation and Annotation of Object Segmentation Datasets Using Robotic Arm

The proposed automated dataset annotation can be a good alternative to manual labeling for object segmentation datasets and introduces a novel application of the digital twin paradigm extending the concept to the field of machine learning dataset generation.

Real-time synthetic-to-real human detection for robotics applications

The target of this work is to assess the generalization of the model trained on synthetic data, to real data, and also to explore the effect of using (few) real images in the training phase, to beneficially affect the performance of the synthetic-to-real real-time model.

ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and Response with AI

The free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS during local and beyond-line-of-site missions is developed and tested.

References

SHOWING 1-10 OF 28 REFERENCES

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

A novel architecture, MiniNet-v2, an enhanced version of MiniNet is developed, built considering the best option depending on CPU or GPU availability, which reaches comparable accuracy to the state-of-the-art models but uses less memory and computational resources.

The Role of Context for Object Detection and Semantic Segmentation in the Wild

A novel deformable part-based model is proposed, which exploits both local context around each candidate detection as well as global context at the level of the scene, which significantly helps in detecting objects at all scales.

Real-Time Semantic Mapping for Autonomous Off-Road Navigation

A semantic mapping system for autonomous off-road driving with an All-Terrain Vehicle (ATVs) to provide a richer representation of the environment than a purely geometric map, allowing it to distinguish, e.g., tall grass from obstacles.

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes

Novel deep dual-resolution networks (DDRNets) are proposed for real-time semantic segmentation of road scenes and a new contextual information extractor named Deep Aggregation Pyramid Pooling Module (DAPPM) is designed to enlarge effective receptive fields and fuse multi-scale context.

Survey on semantic segmentation using deep learning techniques

ERFNet: Efficient Residual Factorized ConvNet for Real-Time Semantic Segmentation

A deep architecture that is able to run in real time while providing accurate semantic segmentation, and a novel layer that uses residual connections and factorized convolutions in order to remain efficient while retaining remarkable accuracy is proposed.

Semantic Understanding of Scenes Through the ADE20K Dataset

This work presents a densely annotated dataset ADE20K, which spans diverse annotations of scenes, objects, parts of objects, and in some cases even parts of parts, and shows that the networks trained on this dataset are able to segment a wide variety of scenes and objects.

LiteSeg: A Novel Lightweight ConvNet for Semantic Segmentation

A new deeper version of Atrous Spatial Pyramid Pooling module (ASPP) is explored and applied short and long residual connections, and depthwise separable convolution, resulting in a faster and efficient model for semantic image segmentation.

BiSeNet: Bilateral Segmentation Network for Real-time Semantic Segmentation

A novel Bilateral Segmentation Network (BiSeNet) is proposed that makes a right balance between the speed and segmentation performance on Cityscapes, CamVid, and COCO-Stuff datasets.

Object-Contextual Representations for Semantic Segmentation

This paper addresses the semantic segmentation problem with a focus on the context aggregation strategy, and presents a simple yet effective approach, object-contextual representations, characterizing a pixel by exploiting the representation of the corresponding object class.