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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 151160 of 259 papers

TitleStatusHype
Contour Proposal Networks for Biomedical Instance SegmentationCode1
Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsCode2
USB: Universal-Scale Object Detection Benchmark0
Object Detection and Pose Estimation from RGB and Depth Data for Real-time, Adaptive Robotic GraspingCode1
CFC-Net: A Critical Feature Capturing Network for Arbitrary-Oriented Object Detection in Remote Sensing ImagesCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
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
Scaled-YOLOv4: Scaling Cross Stage Partial NetworkCode1
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