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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 111120 of 259 papers

TitleStatusHype
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
Quantizing YOLOv7: A Comprehensive Study0
YOLO11 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once (YOLO) Series0
Real-Time Automated donning and doffing detection of PPE based on Yolov4-tiny0
SlowPerception: Physical-World Latency Attack against Visual Perception in Autonomous Driving0
Real-time object detection and tracking using flash LiDAR imagery0
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