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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

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Showing 251–259 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
DroNet: Efficient convolutional neural network detector for real-time UAV applicationsCode0
PVANet: Lightweight Deep Neural Networks for Real-time Object DetectionCode0
PVANET: Deep but Lightweight Neural Networks for Real-time Object DetectionCode0
A Unified Multi-scale Deep Convolutional Neural Network for Fast Object DetectionCode0
YOLOv11: An Overview of the Key Architectural EnhancementsCode0
Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object DetectionCode0
Performance Evaluation of Real-Time Object Detection for Electric ScootersCode0
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