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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
Automated detection of COVID-19 cases using deep neural networks with X-ray imagesCode1
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor DetectionCode1
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
End-to-End Object Detection with TransformersCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
Towards Light-weight and Real-time Line Segment DetectionCode1
CBNet: A Composite Backbone Network Architecture for Object DetectionCode1
You Only Look Once: Unified, Real-Time Object DetectionCode1
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