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Real-Time Object Detection
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Real-Time Object Detection is a computer vision task that involves identifying and locating objects of interest in real-time video sequences with fast inference while maintaining a base level of accuracy.
This is typically solved using algorithms that combine object detection and tracking techniques to accurately detect and track objects in real-time. They use a combination of feature extraction, object proposal generation, and classification to detect and localize objects of interest.
<span style="color:grey; opacity: 0.6">( Image credit: CenterNet )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception
How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection
WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection
A Decade of You Only Look Once (YOLO) for Object Detection
A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions
ATLASv2: LLM-Guided Adaptive Landmark Acquisition and Navigation on the Edge
RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature Learning
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems
HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection
YOLOv12: A Breakdown of the Key Architectural Features
YOLOv4: A Breakthrough in Real-Time Object Detection
Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating Room
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-ID
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection
CorrDiff: Adaptive Delay-aware Detector with Temporal Cue Inputs for Real-time Object Detection
Exploring Machine Learning Engineering for Object Detection and Tracking by Unmanned Aerial Vehicle (UAV)
DEIM: DETR with Improved Matching for Fast Convergence
Optimizing Edge Offloading Decisions for Object Detection
YOLOv11: An Overview of the Key Architectural Enhancements
P-YOLOv8: Efficient and Accurate Real-Time Detection of Distracted Driving
D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution Refinement
Human-in-the-loop Reasoning For Traffic Sign Detection: Collaborative Approach Yolo With Video-llava
Fast Object Detection with a Machine Learning Edge Device
What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector
Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an example
Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approach
DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios
Network transferability of adversarial patches in real-time object detection
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