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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 181190 of 259 papers

TitleStatusHype
PP-YOLOv2: A Practical Object Detector0
USB: Universal-Scale Object Detection Benchmark0
Screening COVID-19 cases using Deep Neural Networks with X-ray images0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG190
Modality-Buffet for Real-Time Object Detection0
Real-time object detection method based on improved YOLOv4-tinyCode0
Fast Object Detection with Latticed Multi-Scale Feature Fusion0
R-TOD: Real-Time Object Detector with Minimized End-to-End Delay for Autonomous DrivingCode0
Artificial Intelligence Enabled Traffic Monitoring System0
Globally-scalable Automated Target Recognition (GATR)0
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