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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 101110 of 259 papers

TitleStatusHype
YOLOv6 v3.0: A Full-Scale ReloadingCode5
DaDe: Delay-adaptive Detector for Streaming PerceptionCode0
RTMDet: An Empirical Study of Designing Real-Time Object DetectorsCode4
DAMO-YOLO : A Report on Real-Time Object Detection DesignCode4
Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation0
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy0
Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis0
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications0
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight AttentionCode1
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing ImageryCode2
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