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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 91100 of 259 papers

TitleStatusHype
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute MechanismCode0
Relief R-CNN : Utilizing Convolutional Features for Fast Object DetectionCode0
Receptive Field Block Net for Accurate and Fast Object DetectionCode0
Real Time Object Detection System with YOLO and CNN Models: A ReviewCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
Replication Study and Benchmarking of Real-Time Object Detection ModelsCode0
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous DrivingCode0
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute SystemsCode0
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating RoomCode0
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in VideoCode0
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