Sirigineedi et al., 2023 - Google Patents
Deep learning approaches for autonomous driving to detect traffic SignsSirigineedi et al., 2023
- Document ID
- 2927606806250973345
- Author
- Sirigineedi M
- Kumaravel T
- Natesan P
- Shruthi V
- Kowsalya M
- Malarkodi M
- Publication year
- Publication venue
- 2023 International Conference on Sustainable Communication Networks and Application (ICSCNA)
External Links
Snippet
Traffic signs serve a vital function in regulating traffic flow, ensuring driver compliance with rules, and ultimately, enhancing road safety by reducing accidents and fatalities. The effective management of traffic signs, especially through automated Identification and …
- 238000013135 deep learning 0 title abstract description 15
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
- G06K9/62—Methods or arrangements for recognition using electronic means
- G06K9/6267—Classification techniques
- G06K9/6268—Classification techniques relating to the classification paradigm, e.g. parametric or non-parametric approaches
-
- 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
- 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
- G06K9/4604—Detecting partial patterns, e.g. edges or contours, or configurations, e.g. loops, corners, strokes, intersections
-
- 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
- G06K9/6267—Classification techniques
- G06K9/6279—Classification techniques relating to the number of classes
-
- 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
- G06K9/6288—Fusion techniques, i.e. combining data from various sources, e.g. sensor fusion
-
- 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
- G06K9/68—Methods or arrangements for recognition using electronic means using sequential comparisons of the image signals with a plurality of references in which the sequence of the image signals or the references is relevant, e.g. addressable memory
-
- 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
- G06K9/6201—Matching; Proximity measures
- G06K9/6202—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
-
- 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
-
- 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
- G06K9/00221—Acquiring or recognising human faces, facial parts, facial sketches, facial expressions
-
- 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
-
- 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
- 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
- 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 |
|---|---|---|
| Romdhane et al. | An improved traffic signs recognition and tracking method for driver assistance system | |
| Kim et al. | Multi-task convolutional neural network system for license plate recognition | |
| Chen et al. | Corse-to-fine road extraction based on local Dirichlet mixture models and multiscale-high-order deep learning | |
| CN107025442B (en) | A multimodal fusion gesture recognition method based on color and depth information | |
| Athira et al. | Underwater object detection model based on YOLOv3 architecture using deep neural networks | |
| Sirigineedi et al. | Deep learning approaches for autonomous driving to detect traffic Signs | |
| CN101609509A (en) | An image object detection method and system based on a pre-classifier | |
| Mujtaba et al. | An Automatic Traffic Control System over Aerial Dataset via U-Net and CNN Model | |
| Le et al. | Bayesian Gabor network with uncertainty estimation for pedestrian lane detection in assistive navigation | |
| Nguyen | Fast traffic sign detection approach based on lightweight network and multilayer proposal network | |
| Bouazizi et al. | Road object detection using SSD-MobileNet algorithm: Case study for real-time ADAS applications | |
| Tyagi et al. | Hybrid FAST-SIFT-CNN (HFSC) approach for vision-based Indian sign language recognition | |
| Liu et al. | Generating pixel enhancement for road extraction in high-resolution aerial images | |
| Ankareddy et al. | Dense segmentation techniques using deep learning for urban scene parsing: a review | |
| Saha et al. | Transfer Learning–A Comparative Analysis | |
| Agunbiade et al. | Enhancement performance of road recognition system of autonomous robots in shadow scenario | |
| Alom et al. | Enhanced road lane marking detection system: A cnn-based approach for safe driving | |
| Balaji et al. | Deep learning technique for effective segmentation of water bodies using remote sensing images | |
| Kiruthika Devi et al. | A deep learning-based residual network model for traffic sign detection and classification | |
| Balasundaram et al. | Implementation of next-generation traffic sign recognition system with two-tier classifier architecture | |
| Singh et al. | Comprehensive Approach to Road Sign Detection and Recognition for Autonomous Driving Systems and Road Safety | |
| Hamdy et al. | Performance Evaluation of Artificial Neural Network Methods Based on Block Machine Learning Classification | |
| Martin Sagayam et al. | Application of pseudo 2-D hidden Markov model for hand gesture recognition | |
| Kazbekova et al. | Real-Time Lightweight Sign Language Recognition on Hybrid Deep CNN-BiLSTM Neural Network with Attention Mechanism. | |
| Mo et al. | Deep semantic segmentation for drivable area detection on unstructured roads |