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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 111120 of 259 papers

TitleStatusHype
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)0
YOLOv6: A Single-Stage Object Detection Framework for Industrial ApplicationsCode5
FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications0
Image Models for large-scale Object Detection and Classification0
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey0
Real Time Object Detection System with YOLO and CNN Models: A ReviewCode0
StreamYOLO: Real-time Object Detection for Streaming Perception0
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectorsCode7
YOLOSA: Object detection based on 2D local feature superimposed self-attentionCode0
0/1 Deep Neural Networks via Block Coordinate Descent0
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