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Real-Time Object Detection

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.

( Image credit: CenterNet )

Papers

Showing 4150 of 259 papers

TitleStatusHype
DART: An Automated End-to-End Object Detection Pipeline with Data Diversification, Open-Vocabulary Bounding Box Annotation, Pseudo-Label Review, and Model TrainingCode1
Quantizing YOLOv7: A Comprehensive Study0
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detectionCode2
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series0
Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny0
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving0
Real-time object detection and tracking using flash LiDAR imagery0
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
LW-DETR: A Transformer Replacement to YOLO for Real-Time DetectionCode9
Precision and Adaptability of YOLOv5 and YOLOv8 in Dynamic Robotic Environments0
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