Real-Time Object Detection
110 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
BED: A Real-Time Object Detection System for Edge Devices
DNNs have been an effective tool for data processing and analysis.
A ConvNet for the 2020s
keras-team/keras
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CVPR 2022
The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
YOLO-ReT: Towards High Accuracy Real-time Object Detection on Edge GPUs
Performance of object detection models has been growing rapidly on two major fronts, model accuracy and efficiency.
Non-deep Networks
imankgoyal/NonDeepNetworks
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This begs the question -- is it possible to build high-performing "non-deep" neural networks?
FOVEA: Foveated Image Magnification for Autonomous Navigation
Efficient processing of high-res video streams is safety-critical for many robotics applications such as autonomous driving.
Detectron2 Object Detection & Manipulating Images using Cartoonization
facebookresearch/detectron2
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International Journal of Engineering Research & Technology (IJERT) 2021
In today's world, there is a rapid increase in the autonomous vehicle.
Workshop on Autonomous Driving at CVPR 2021: Technical Report for Streaming Perception Challenge
Megvii-BaseDetection/YOLOX
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In this report, we introduce our real-time 2D object detection system for the realistic autonomous driving scenario.
Parallel Detection for Efficient Video Analytics at the Edge
A common performance requirement in these mission-critical edge services is the near real-time latency of online object detection on edge devices.
YOLOX: Exceeding YOLO Series in 2021
In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
CBNet: A Composite Backbone Network Architecture for Object Detection
PaddlePaddle/PaddleDetection
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With multi-scale testing, we push the current best single model result to a new record of 60. 1% box AP and 52. 3% mask AP without using extra training data.
