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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 3140 of 259 papers

TitleStatusHype
Fast Object Detection with a Machine Learning Edge Device0
What is YOLOv9: An In-Depth Exploration of the Internal Features of the Next-Generation Object Detector0
Transtreaming: Adaptive Delay-aware Transformer for Real-time Streaming Perception0
Real-Time Dynamic Scale-Aware Fusion Detection Network: Take Road Damage Detection as an exampleCode0
Real-Time Indoor Object Detection based on hybrid CNN-Transformer Approach0
DS MYOLO: A Reliable Object Detector Based on SSMs for Driving Scenarios0
Network transferability of adversarial patches in real-time object detectionCode0
YOLOv1 to YOLOv10: The fastest and most accurate real-time object detection systems0
An Efficient Real-Time Object Detection Framework on Resource-Constricted Hardware Devices via Software and Hardware Co-design0
Octave-YOLO: Cross frequency detection network with octave convolution0
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