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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
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionCode0
DaDe: Delay-adaptive Detector for Streaming PerceptionCode0
PVANet: Lightweight Deep Neural Networks for Real-time Object DetectionCode0
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an exampleCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
SlimYOLOv3: Narrower, Faster and Better for Real-Time UAV ApplicationsCode0
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
Pelee: A Real-Time Object Detection System on Mobile DevicesCode0
Fast object detection in compressed JPEG ImagesCode0
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