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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 110 of 259 papers

TitleStatusHype
Detection of Rail Line Track and Human Beings Near the Track to Avoid Accidents0
YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual PerceptionCode5
How Real is CARLAs Dynamic Vision Sensor? A Study on the Sim-to-Real Gap in Traffic Object Detection0
WD-DETR: Wavelet Denoising-Enhanced Real-Time Object Detection Transformer for Robot Perception with Event Cameras0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection0
WTEFNet: Real-Time Low-Light Object Detection for Advanced Driver-Assistance Systems0
CF-DETR: Coarse-to-Fine Transformer for Real-Time Object Detection0
A Decade of You Only Look Once (YOLO) for Object Detection0
A Vision-Enabled Prosthetic Hand for Children with Upper Limb Disabilities0
A Review of YOLOv12: Attention-Based Enhancements vs. Previous Versions0
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