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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 191200 of 259 papers

TitleStatusHype
Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules0
Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation0
Industrial object, machine part and defect recognition towards fully automated industrial monitoring employing deep learning. The case of multilevel VGG190
Inference of Quantized Neural Networks on Heterogeneous All-Programmable Devices0
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving0
Lidar based Detection and Classification of Pedestrians and Vehicles Using Machine Learning Methods0
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer0
Liquid Leak Detection Using Thermal Images0
MambaNeXt-YOLO: A Hybrid State Space Model for Real-time Object Detection0
Modality-Buffet for Real-Time Object Detection0
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