[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Sellat et al., 2022 - Google Patents

Intelligent Semantic Segmentation for Self‐Driving Vehicles Using Deep Learning

Sellat et al., 2022

View PDF @Full View
Document ID
7306488687302791890
Author
Sellat Q
Bisoy S
Priyadarshini R
Vidyarthi A
Kautish S
Barik R
Publication year
Publication venue
Computational Intelligence and Neuroscience

External Links

Snippet

Understanding the situation is a critical component of any self‐driving system. Accurate real‐ time visual signal processing to create pixelwise classed pictures, also known as semantic segmentation, is critical for scenario comprehension and subsequent acceptance of this new …
Continue reading at onlinelibrary.wiley.com (PDF) (other versions)

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
    • G06K9/00624—Recognising scenes, i.e. recognition of a whole field of perception; recognising scene-specific objects
    • 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
    • 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
    • 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
    • G06K9/32—Aligning or centering of the image pick-up or image-field
    • G06K9/3233—Determination of region of interest
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • 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

Similar Documents

Publication Publication Date Title
Mohan et al. Efficientps: Efficient panoptic segmentation
Sellat et al. Intelligent Semantic Segmentation for Self‐Driving Vehicles Using Deep Learning
Garcia-Garcia et al. A survey on deep learning techniques for image and video semantic segmentation
Galvao et al. Pedestrian and vehicle detection in autonomous vehicle perception systems—A review
Khalid et al. Automatic measurement of the traffic sign with digital segmentation and recognition<? show [AQ ID= Q1]?>
Shojaiee et al. EFASPP U-Net for semantic segmentation of night traffic scenes using fusion of visible and thermal images
Sang et al. Small-object sensitive segmentation using across feature map attention
Zhou et al. Deep road scene understanding
Liu et al. Improved GBS-YOLOv5 algorithm based on YOLOv5 applied to UAV intelligent traffic
Liu et al. Image-free single-pixel segmentation
Van Quyen et al. Feature pyramid network with multi-scale prediction fusion for real-time semantic segmentation
Yang et al. An efficient semantic segmentation method based on transfer learning from object detection
Arulananth et al. Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis
Liang et al. Car detection and classification using cascade model
Wang Remote sensing image semantic segmentation algorithm based on improved ENet network
Nie et al. MIGN: Multiscale image generation network for remote sensing image semantic segmentation
Yasmin et al. Small obstacles detection on roads scenes using semantic segmentation for the safe navigation of autonomous vehicles
Nguyen et al. Smart solution to detect images in limited visibility conditions based convolutional neural networks
Choi et al. ADFNet: accumulated decoder features for real‐time semantic segmentation
Li et al. Efficient residual neural network for semantic segmentation
Feng et al. A multibranch object detection method for traffic scenes
Boisclair et al. Attention transfer from human to neural networks for road object detection in winter
Yin et al. Road extraction from satellite images with iterative cross-task feature enhancement
Zhang et al. DNet-CNet: A novel cascaded deep network for real-time lane detection and classification
Sellat et al. Research Article Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning