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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 7180 of 259 papers

TitleStatusHype
First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria0
Liquid Leak Detection Using Thermal Images0
Object-centric Cross-modal Feature Distillation for Event-based Object Detection0
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLOCode1
HIC-YOLOv5: Improved YOLOv5 For Small Object DetectionCode1
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor DetectionCode1
DEYOv3: DETR with YOLO for Real-time Object DetectionCode0
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute MechanismCode0
Real-Time Object Detection on High-Voltage Powerlines Using an Unmanned Aerial Vehicle (UAV)Code0
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images0
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