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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
ATLASv2: LLM-Guided Adaptive Landmark Acquisition and Navigation on the Edge0
RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature LearningCode1
Enabling Efficient Processing of Spiking Neural Networks with On-Chip Learning on Commodity Neuromorphic Processors for Edge AI Systems0
HGO-YOLO: Advancing Anomaly Behavior Detection with Hierarchical Features and Lightweight Optimized Detection0
2D Object Detection: A Survey0
YOLOv12: A Breakdown of the Key Architectural Features0
YOLOv4: A Breakthrough in Real-Time Object Detection0
Adaptive Object Detection for Indoor Navigation Assistance: A Performance Evaluation of Real-Time Algorithms0
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating RoomCode0
TD-RD: A Top-Down Benchmark with Real-Time Framework for Road Damage Detection0
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