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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 6170 of 259 papers

TitleStatusHype
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer0
YOLOv9: Learning What You Want to Learn Using Programmable Gradient InformationCode16
Context-aware Multi-Model Object Detection for Diversely Heterogeneous Compute SystemsCode0
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLOCode1
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm SurveillanceCode0
Real-time Traffic Object Detection for Autonomous Driving0
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach0
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion0
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning0
BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge0
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