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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 6170 of 259 papers

TitleStatusHype
YOLO9000: Better, Faster, StrongerCode1
Joint Anchor-Feature Refinement for Real-Time Accurate Object Detection in Images and VideosCode1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor DetectionCode1
BED: A Real-Time Object Detection System for Edge DevicesCode1
RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature LearningCode1
Scaled-YOLOv4: Scaling Cross Stage Partial NetworkCode1
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight AttentionCode1
End-to-End Object Detection with TransformersCode1
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