Fan et al., 2021 - Google Patents
Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural NetworkFan et al., 2021
View PDF- Document ID
- 79183769360523260
- Author
- Fan Y
- Yelamandala C
- Chen T
- Huang C
- Publication year
- Publication venue
- Journal of Sensors
External Links
Snippet
Recently, self‐driving cars became a big challenge in the automobile industry. After the DARPA challenge, which introduced the design of a self‐driving system that can be classified as SAR Level 3 or higher levels, driven to focus on self‐driving cars more. Later …
- 238000001514 detection method 0 title abstract description 46
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
- G06K9/20—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
- 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
- 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
- G06K9/6217—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
- G06T7/00—Image analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06K—RECOGNITION OF DATA; PRESENTATION OF DATA; RECORD CARRIERS; HANDLING RECORD CARRIERS
- G06K2209/00—Indexing scheme relating to methods or arrangements for reading or recognising printed or written characters or for recognising patterns, e.g. fingerprints
-
- 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
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Fan et al. | Real‐Time Object Detection for LiDAR Based on LS‐R‐YOLOv4 Neural Network | |
Huang et al. | Autonomous driving with deep learning: A survey of state-of-art technologies | |
Zhang et al. | Real-time detection method for small traffic signs based on Yolov3 | |
Zhu et al. | Overview of environment perception for intelligent vehicles | |
Sun et al. | 3-D data processing to extract vehicle trajectories from roadside LiDAR data | |
Hoque et al. | A comprehensive review on 3D object detection and 6D pose estimation with deep learning | |
Peng et al. | MASS: Multi-attentional semantic segmentation of LiDAR data for dense top-view understanding | |
Ma et al. | Capsule-based networks for road marking extraction and classification from mobile LiDAR point clouds | |
Wang et al. | Speed and accuracy tradeoff for LiDAR data based road boundary detection | |
Nguyen et al. | Real-time vehicle detection using an effective region proposal-based depth and 3-channel pattern | |
Dewangan et al. | Towards the design of vision-based intelligent vehicle system: methodologies and challenges | |
Nie et al. | 3D object detection and tracking based on lidar-camera fusion and IMM-UKF algorithm towards highway driving | |
Mauri et al. | Lightweight convolutional neural network for real-time 3D object detection in road and railway environments | |
Vajak et al. | Recent advances in vision-based lane detection solutions for automotive applications | |
Wu et al. | Realtime single-shot refinement neural network with adaptive receptive field for 3D object detection from LiDAR point cloud | |
Huang et al. | SSA3D: Semantic segmentation assisted one-stage three-dimensional vehicle object detection | |
US20240010225A1 (en) | Representation learning for object detection from unlabeled point cloud sequences | |
Yuan et al. | Multi-level object detection by multi-sensor perception of traffic scenes | |
Jang et al. | Real-time driving scene understanding via efficient 3-D LiDAR processing | |
Stäcker et al. | RC-BEVFusion: A plug-in module for radar-camera bird’s eye view feature fusion | |
Chen et al. | A semantic segmentation method for vehicle‐borne laser scanning point clouds in motorway scenes | |
Juyal et al. | Object Classification Using A rtificial I ntelligence Technique sin Autonomous Vehicles | |
Zhao et al. | DHA: Lidar and vision data fusion-based on road object classifier | |
Imad et al. | Navigation system for autonomous vehicle: A survey | |
Vellaidurai et al. | A novel oyolov5 model for vehicle detection and classification in adverse weather conditions |