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
| 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
Most implemented papers
An Energy and GPU-Computation Efficient Backbone Network for Real-Time Object Detection
osmr/imgclsmob
•
•
As DenseNet conserves intermediate features with diverse receptive fields by aggregating them with dense connection, it shows good performance on the object detection task.
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
IDEACVR/DINO
•
•
Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving
In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
tensorflow/models
•
•
CVPR 2020
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
In object detection, reducing computational cost is as important as improving accuracy for most practical usages.
Pelee: A Real-Time Object Detection System on Mobile Devices
PaddlePaddle/PaddleClas
•
•
NeurIPS 2018
In this study, we propose an efficient architecture named PeleeNet, which is built with conventional convolution instead.
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
sacmehta/EdgeNets
•
•
CVPR 2019
Compared to YOLOv2 on the MS-COCO object detection, ESPNetv2 delivers 4. 4% higher accuracy with 6x fewer FLOPs.
You Only Learn One Representation: Unified Network for Multiple Tasks
WongKinYiu/yolor
•
•
In this paper, we propose a unified network to encode implicit knowledge and explicit knowledge together, just like the human brain can learn knowledge from normal learning as well as subconsciousness learning.
RTMDet: An Empirical Study of Designing Real-Time Object Detectors
In this paper, we aim to design an efficient real-time object detector that exceeds the YOLO series and is easily extensible for many object recognition tasks such as instance segmentation and rotated object detection.
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
open-mmlab/mmdetection
•
•
CVPR 2019
Here we aim to learn a better architecture of feature pyramid network for object detection.
