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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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal NetworksCode1
CSPNet: A New Backbone that can Enhance Learning Capability of CNNCode1
Parallel Residual Bi-Fusion Feature Pyramid Network for Accurate Single-Shot Object DetectionCode1
RT-DATR:Real-time Unsupervised Domain Adaptive Detection Transformer with Adversarial Feature LearningCode1
Contour Proposal Networks for Biomedical Instance SegmentationCode1
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight AttentionCode1
Non-deep NetworksCode1
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object DetectionCode1
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin TransformerCode1
Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged NetworksCode1
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