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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 4150 of 259 papers

TitleStatusHype
YUDO: YOLO for Uniform Directed Object DetectionCode1
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor DetectionCode1
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin TransformerCode1
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and MethodCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection StrategyCode1
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight AttentionCode1
BED: A Real-Time Object Detection System for Edge DevicesCode1
RegionCLIP: Region-based Language-Image PretrainingCode1
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUsCode1
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