Real-Time Object Detection
107 papers with code • 7 benchmarks • 8 datasets
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 )
Benchmarks
These leaderboards are used to track progress in Real-Time Object Detection
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Libraries
Use these libraries to find Real-Time Object Detection models and implementationsDatasets
MS COCO
Kvasir-SEG
PASCAL VOC 2007
Kvasir
Argoverse-HD
Kvasir-Instrument
Hyper-Kvasir Dataset
Endotect Polyp Segmentation Challenge Dataset
Latest papers with no code
GLCM-Based Feature Combination for Extraction Model Optimization in Object Detection Using Machine Learning
Therefore, based on the trade-off between accuracy and complexity, the K-NN model with a combination of Correlation, Energy, and Homogeneity features emerges as a more suitable choice for real-time applications that demand high accuracy and low complexity.
Prototipo de un Contador Bidireccional Automático de Personas basado en sensores de visión 3D
The described prototype uses RGB-D sensors for bidirectional people counting in venues, aiding security and surveillance in spaces like stadiums or airports.
Inception-YOLO: Computational cost and accuracy improvement of the YOLOv5 model based on employing modified CSP, SPPF, and inception modules
However, these methods are not the most efficient architectures, and there is always room to improve.
Lightweight Object Detection: A Study Based on YOLOv7 Integrated with ShuffleNetv2 and Vision Transformer
As mobile computing technology rapidly evolves, deploying efficient object detection algorithms on mobile devices emerges as a pivotal research area in computer vision.
Real-time Traffic Object Detection for Autonomous Driving
In this research, we assess the robustness of our previously proposed, highly efficient pedestrian detector LSFM on well-established autonomous driving benchmarks, including diverse weather conditions and nighttime scenes.
Real-time object detection and robotic manipulation for agriculture using a YOLO-based learning approach
The optimisation of crop harvesting processes for commonly cultivated crops is of great importance in the aim of agricultural industrialisation.
Small Object Detection by DETR via Information Augmentation and Adaptive Feature Fusion
This allows the model to adaptively fuse feature maps from different levels and effectively integrate feature information from different scales.
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning
The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4.
BiSwift: Bandwidth Orchestrator for Multi-Stream Video Analytics on Edge
To achieve this goal, we propose BiSwift, a bi-level framework that scales the concurrent real-time video analytics by a novel adaptive hybrid codec integrated with multi-level pipelines, and a global bandwidth controller for multiple video streams.
First qualitative observations on deep learning vision model YOLO and DETR for automated driving in Austria
This study investigates the application of single and two-stage 2D-object detection algorithms like You Only Look Once (YOLO), Real-Time DEtection TRansformer (RT-DETR) algorithm for automated object detection to enhance road safety for autonomous driving on Austrian roads.
