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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 7180 of 259 papers

TitleStatusHype
Scaled-YOLOv4: Scaling Cross Stage Partial NetworkCode1
Non-deep NetworksCode1
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor DetectionCode1
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
End-to-End Object Detection with TransformersCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
RegionCLIP: Region-based Language-Image PretrainingCode1
Towards Light-weight and Real-time Line Segment DetectionCode1
You Only Look Once: Unified, Real-Time Object DetectionCode1
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