SOTAVerified

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 241250 of 259 papers

TitleStatusHype
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute MechanismCode0
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in VideoCode0
Real-Time Brain Tumor Detection in Intraoperative Ultrasound Using YOLO11: From Model Training to Deployment in the Operating RoomCode0
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV ApplicationsCode0
Fast object detection in compressed JPEG ImagesCode0
BlitzNet: A Real-Time Deep Network for Scene UnderstandingCode0
YOLO-LITE: A Real-Time Object Detection Algorithm Optimized for Non-GPU ComputersCode0
Fast and accurate object detection in high resolution 4K and 8K video using GPUsCode0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
YOLOSA: Object detection based on 2D local feature superimposed self-attentionCode0
Show:102550
← PrevPage 25 of 26Next →

No leaderboard results yet.