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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 121130 of 259 papers

TitleStatusHype
SpineNet: Learning Scale-Permuted Backbone for Recognition and LocalizationCode0
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous DrivingCode0
YOLOSA: Object detection based on 2D local feature superimposed self-attentionCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
Real-time object detection method based on improved YOLOv4-tinyCode0
DaDe: Delay-adaptive Detector for Streaming PerceptionCode0
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in VideoCode0
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionCode0
PVANet: Lightweight Deep Neural Networks for Real-time Object DetectionCode0
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural NetworkCode0
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