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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 8190 of 259 papers

TitleStatusHype
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object DetectionCode2
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving0
YUDO: YOLO for Uniform Directed Object DetectionCode1
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor DetectionCode1
BandRe: Rethinking Band-Pass Filters for Scale-Wise Object Detection EvaluationCode0
Q-YOLO: Efficient Inference for Real-time Object Detection0
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin TransformerCode1
Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv80
Large, Complex, and Realistic Safety Clothing and Helmet Detection: Dataset and MethodCode1
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion DetectionCode1
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