Sgibnev et al., 2020 - Google Patents
Deep semantic segmentation for the off-road autonomous drivingSgibnev et al., 2020
View PDF- Document ID
- 16540849585172126752
- Author
- Sgibnev I
- Sorokin A
- Vishnyakov B
- Vizilter Y
- Publication year
- Publication venue
- The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
External Links
Snippet
This paper is devoted to the problem of image semantic segmentation for machine vision system of off-road autonomous robotic vehicle. Most modern convolutional neural networks require large computing resources that go beyond the capabilities of many robotic platforms …
- 230000011218 segmentation 0 title abstract description 30
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/36—Image preprocessing, i.e. processing the image information without deciding about the identity of the image
- G06K9/46—Extraction of features or characteristics of the image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6217—Design or setup of recognition systems and techniques; Extraction of features in feature space; Clustering techniques; Blind source separation
- G06K9/6256—Obtaining sets of training patterns; Bootstrap methods, e.g. bagging, boosting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/0063—Recognising patterns in remote scenes, e.g. aerial images, vegetation versus urban areas
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
- G06K9/00791—Recognising scenes perceived from the perspective of a land vehicle, e.g. recognising lanes, obstacles or traffic signs on road scenes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/20—Special algorithmic details
- G06T2207/20112—Image segmentation details
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30181—Earth observation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K9/00—Methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
- G06K9/20—Image acquisition
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/20—Analysis of motion
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
- G06T17/05—Geographic models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30781—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F17/30784—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre
- G06F17/30799—Information retrieval; Database structures therefor; File system structures therefor of video data using features automatically derived from the video content, e.g. descriptors, fingerprints, signatures, genre using low-level visual features of the video content
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mei et al. | CoANet: Connectivity attention network for road extraction from satellite imagery | |
| Pham et al. | Road damage detection and classification with YOLOv7 | |
| Garcia-Garcia et al. | A review on deep learning techniques applied to semantic segmentation | |
| Zhao et al. | Building extraction from satellite images using mask R-CNN with building boundary regularization | |
| Caltagirone et al. | Fast LIDAR-based road detection using fully convolutional neural networks | |
| Audebert et al. | Distance transform regression for spatially-aware deep semantic segmentation | |
| Li et al. | Dual-view 3d object recognition and detection via lidar point cloud and camera image | |
| CN116503602A (en) | Unstructured environment three-dimensional point cloud semantic segmentation method based on multi-level edge enhancement | |
| CN111968133A (en) | Three-dimensional point cloud data example segmentation method and system in automatic driving scene | |
| Metzger et al. | A fine-grained dataset and its efficient semantic segmentation for unstructured driving scenarios | |
| US20200250499A1 (en) | Method for integrating driving images acquired from vehicles performing cooperative driving and driving image integrating device using same | |
| Sgibnev et al. | Deep semantic segmentation for the off-road autonomous driving | |
| CN113671522B (en) | Dynamic environment laser SLAM method based on semantic constraint | |
| Anilkumar et al. | An adaptive DeepLabv3+ for semantic segmentation of aerial images using improved golden eagle optimization algorithm | |
| Abdulghafoor et al. | Real-time moving objects detection and tracking using deep-stream technology | |
| Hoang et al. | Lane road segmentation based on improved UNET architecture for autonomous driving | |
| Bieder et al. | Exploiting multi-layer grid maps for surround-view semantic segmentation of sparse lidar data | |
| Musa et al. | A theoretical framework towards building a lightweight model for pothole detection using knowledge distillation approach | |
| Kampffmeyer et al. | Dense dilated convolutions merging network for semantic mapping of remote sensing images | |
| Usmani et al. | Towards global scale segmentation with openstreetmap and remote sensing | |
| Zhong et al. | 3d geometry-aware semantic labeling of outdoor street scenes | |
| Li et al. | Strip and asymmetric aggregation network for unstructured terrain segmentation in wild environments | |
| Chaoju et al. | Road segmentation algorithm based on improved YOLOv8-seg | |
| Xue et al. | Multiscale feature extraction network for real-time semantic segmentation of road scenes on the autonomous robot | |
| Shashaani et al. | Using layer-wise training for road semantic segmentation in autonomous cars |