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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 251259 of 259 papers

TitleStatusHype
xYOLO: A Model For Real-Time Object Detection In Humanoid Soccer On Low-End Hardware0
Yes-Net: An effective Detector Based on Global Information0
YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective0
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series0
YOLOv12: A Breakdown of the Key Architectural Features0
YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems0
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)0
YOLOv4: A Breakthrough in Real-Time Object Detection0
YotoR-You Only Transform One Representation0
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