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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 125 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
YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsCode5
A ConvNet for the 2020sCode5
RTMDet: An Empirical Study of Designing Real-Time Object DetectorsCode4
DAMO-YOLO : A Report on Real-Time Object Detection DesignCode4
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
Vision Transformer Adapter for Dense PredictionsCode3
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception ChallengeCode3
Deformable DETR: Deformable Transformers for End-to-End Object DetectionCode3
YOLOv4: Optimal Speed and Accuracy of Object DetectionCode3
EfficientDet: Scalable and Efficient Object DetectionCode3
YOLO11-JDE: Fast and Accurate Multi-Object Tracking with Self-Supervised Re-IDCode2
Multi-Branch Auxiliary Fusion YOLO with Re-parameterization Heterogeneous Convolutional for accurate object detectionCode2
FedPylot: Navigating Federated Learning for Real-Time Object Detection in Internet of VehiclesCode2
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