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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 110 of 259 papers

TitleStatusHype
YOLOv9: Learning What You Want to Learn Using Programmable Gradient InformationCode16
YOLOv10: Real-Time End-to-End Object DetectionCode11
LW-DETR: A Transformer Replacement to YOLO for Real-Time DetectionCode9
DETRs Beat YOLOs on Real-time Object DetectionCode8
D-FINE: Redefine Regression Task in DETRs as Fine-grained Distribution RefinementCode7
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsCode7
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual PerceptionCode5
DEIM: DETR with Improved Matching for Fast ConvergenceCode5
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion HeadCode5
YOLOv6 v3.0: A Full-Scale ReloadingCode5
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