Real-Time Object Detection
108 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
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
|||||
|
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
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
The proposed quantized network architecture with 422k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators.
YOLO-Drone:Airborne real-time detection of dense small objects from high-altitude perspective
With regards to UAVDT, the YOLO-Drone exhibits both high real-time inference speed of 53 FPS and a maximum mAP of 34. 04%.
A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS
YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications.
Real-time SLAM Pipeline in Dynamics Environment
Inspired by the recent success of application of dense data approach by using ORB-SLAM and RGB-D SLAM, we propose a better pipeline of real-time SLAM in dynamics environment.
R-TOSS: A Framework for Real-Time Object Detection using Semi-Structured Pruning
In this paper, we introduce a novel semi-structured pruning framework called R-TOSS that overcomes the shortcomings of state-of-the-art model pruning techniques.
Indian Commercial Truck License Plate Detection and Recognition for Weighbridge Automation
Detection and recognition of a licence plate is important when automating weighbridge services.
ROMA: Run-Time Object Detection To Maximize Real-Time Accuracy
This paper analyzes the effects of dynamically varying video contents and detection latency on the real-time detection accuracy of a detector and proposes a new run-time accuracy variation model, ROMA, based on the findings from the analysis.
Automatic Cattle Identification using YOLOv5 and Mosaic Augmentation: A Comparative Analysis
This paper aims to present our recent research in utilizing five popular object detection models, looking at the architecture of YOLOv5, investigating the performance of eight backbones with the YOLOv5 model, and the influence of mosaic augmentation in YOLOv5 by experimental results on the available cattle muzzle images.
Self-Configurable Stabilized Real-Time Detection Learning for Autonomous Driving Applications
In addition, we demonstrate the self-configurable stabilized detection with YOLOv3-tiny and FlowNet2-S, which are the real-time object detection network and an optical flow estimation network, respectively.
YOLO v3: Visual and Real-Time Object Detection Model for Smart Surveillance Systems(3s)
Recent studies have explored several models in object detection; however, most have failed to meet the demand for objectiveness and predictive accuracy, especially in developing and under-developed countries.
