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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 1120 of 259 papers

TitleStatusHype
YOLOv6 v3.0: A Full-Scale ReloadingCode5
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual PerceptionCode5
RTMDet: An Empirical Study of Designing Real-Time Object DetectorsCode4
PP-YOLOE: An evolved version of YOLOCode4
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object DetectionCode4
Detectron2 Object Detection & Manipulating Images using CartoonizationCode4
DAMO-YOLO : A Report on Real-Time Object Detection DesignCode4
YOLOv4: Optimal Speed and Accuracy of Object DetectionCode3
Deformable DETR: Deformable Transformers for End-to-End Object DetectionCode3
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception ChallengeCode3
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