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

Sellat et al., 2022 - Google Patents

Research Article Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning

Sellat et al., 2022

View PDF
Document ID
162657479791675797
Author
Sellat Q
Bisoy S
Priyadarshini R
Vidyarthi A
Kautish S
Barik R
Publication year

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 www.academia.edu (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
    • 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
    • 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
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • 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
    • G06T7/00—Image analysis
    • G06T7/20—Analysis of motion
    • 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
    • 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
    • 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
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass

Similar Documents

Publication Publication Date Title
Mo et al. Review the state-of-the-art technologies of semantic segmentation based on deep learning
Mohan et al. Efficientps: Efficient panoptic segmentation
Azimi et al. Aerial LaneNet: Lane-marking semantic segmentation in aerial imagery using wavelet-enhanced cost-sensitive symmetric fully convolutional neural networks
Sellat et al. Intelligent Semantic Segmentation for Self‐Driving Vehicles Using Deep Learning
Krešo et al. Convolutional scale invariance for semantic segmentation
Hurtado et al. Semantic scene segmentation for robotics
Deshmukh et al. Swin transformer based vehicle detection in undisciplined traffic environment
He et al. Urban rail transit obstacle detection based on Improved R-CNN
Kütük et al. Semantic segmentation for thermal images: A comparative survey
Shojaiee et al. EFASPP U-Net for semantic segmentation of night traffic scenes using fusion of visible and thermal images
US20220156528A1 (en) Distance-based boundary aware semantic segmentation
Sang et al. Small-object sensitive segmentation using across feature map attention
Liu et al. Image-free single-pixel segmentation
Guan et al. Road marking extraction in UAV imagery using attentive capsule feature pyramid network
Singha et al. FANet: Feature aggregation network for semantic segmentation
Zhang et al. Automated pixel-level pavement marking detection based on a convolutional transformer
Hussain et al. Image Segmentation for Autonomous Driving Using U-Net Inception
Ouyang et al. Multiview cnn model for sensor fusion based vehicle detection
Hoang et al. Lane Road Segmentation Based on Improved UNet Architecture for Autonomous Driving
Yin et al. Road extraction from satellite images with iterative cross-task feature enhancement
Choi et al. ADFNet: accumulated decoder features for real‐time semantic segmentation
Sellat et al. Research Article Intelligent Semantic Segmentation for Self-Driving Vehicles Using Deep Learning
WO2022197958A1 (en) Frames alignment in video processing using self-attention
Pihlak et al. Simultaneous road edge and road surface markings detection using convolutional neural networks
Singh et al. Semantic segmentation using deep learning