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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 2130 of 259 papers

TitleStatusHype
YOLOv4: Optimal Speed and Accuracy of Object DetectionCode3
EfficientDet: Scalable and Efficient Object DetectionCode3
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-IDCode2
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detectionCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
YOLOv5-6D: Advancing 6-DoF Instrument Pose Estimation in Variable X-Ray Imaging GeometriesCode2
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object DetectionCode2
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing ImageryCode2
Slim-neck by GSConv: A lightweight-design for real-time detector architecturesCode2
Real-time Object Detection for Streaming PerceptionCode2
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