Real-Time Object Detection
109 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
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.
FasterX: Real-Time Object Detection Based on Edge GPUs for UAV Applications
Real-time object detection on Unmanned Aerial Vehicles (UAVs) is a challenging issue due to the limited computing resources of edge GPU devices as Internet of Things (IoT) nodes.
A Comprehensive Study of Real-Time Object Detection Networks Across Multiple Domains: A Survey
Our extensive empirical study can act as a guideline for the industrial community to make an informed choice on the existing networks.
StreamYOLO: Real-time Object Detection for Streaming Perception
In this paper, we explore the performance of real time models on this metric and endow the models with the capacity of predicting the future, significantly improving the results for streaming perception.
YOLOSA: Object detection based on 2D local feature superimposed self-attention
Applying an attention module here can effectively improve the detection accuracy of the model.
SpikiLi: A Spiking Simulation of LiDAR based Real-time Object Detection for Autonomous Driving
Spiking Neural Networks are a recent and new neural network design approach that promises tremendous improvements in power efficiency, computation efficiency, and processing latency.
Small Object Detection using Deep Learning
The proposed system consists of a custom deep learning model Tiny YOLOv3, one of the flavors of very fast object detection model You Look Only Once (YOLO) is built and used for detection.
Speed Up Object Detection on Gigapixel-Level Images With Patch Arrangement
With the appearance of super high-resolution (e. g., gigapixel-level) images, performing efficient object detection on such images becomes an important issue.
Network-Aware 5G Edge Computing for Object Detection: Augmenting Wearables to "See" More, Farther and Faster
The vision evaluation is combined with a detailed full-stack wireless network simulation to determine the distribution of throughputs and delays with real navigation paths and ray-tracing from new high-resolution 3D models in an urban environment.
Factorial Convolution Neural Networks
In recent years, GoogleNet has garnered substantial attention as one of the base convolutional neural networks (CNNs) to extract visual features for object detection.
