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
Most implemented papers
EfficientDet: Scalable and Efficient Object Detection
Model efficiency has become increasingly important in computer vision.
R-FCN: Object Detection via Region-based Fully Convolutional Networks
daijifeng001/r-fcn
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NeurIPS 2016
In contrast to previous region-based detectors such as Fast/Faster R-CNN that apply a costly per-region subnetwork hundreds of times, our region-based detector is fully convolutional with almost all computation shared on the entire image.
A ConvNet for the 2020s
facebookresearch/ConvNeXt
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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The "Roaring 20s" of visual recognition began with the introduction of Vision Transformers (ViTs), which quickly superseded ConvNets as the state-of-the-art image classification model.
Scaled-YOLOv4: Scaling Cross Stage Partial Network
WongKinYiu/ScaledYOLOv4
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CVPR 2021
We show that the YOLOv4 object detection neural network based on the CSP approach, scales both up and down and is applicable to small and large networks while maintaining optimal speed and accuracy.
YOLOX: Exceeding YOLO Series in 2021
Megvii-BaseDetection/YOLOX
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In this report, we present some experienced improvements to YOLO series, forming a new high-performance detector -- YOLOX.
End-to-End Object Detection with Transformers
facebookresearch/detr
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ECCV 2020
We present a new method that views object detection as a direct set prediction problem.
MnasNet: Platform-Aware Neural Architecture Search for Mobile
In this paper, we propose an automated mobile neural architecture search (MNAS) approach, which explicitly incorporate model latency into the main objective so that the search can identify a model that achieves a good trade-off between accuracy and latency.
HarDNet: A Low Memory Traffic Network
PingoLH/Pytorch-HarDNet
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ICCV 2019
We propose a Harmonic Densely Connected Network to achieve high efficiency in terms of both low MACs and memory traffic.
Deformable DETR: Deformable Transformers for End-to-End Object Detection
fundamentalvision/Deformable-DETR
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ICLR 2021
DETR has been recently proposed to eliminate the need for many hand-designed components in object detection while demonstrating good performance.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
wongkinyiu/yolov7
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CVPR 2023
YOLOv7 surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS and has the highest accuracy 56. 8% AP among all known real-time object detectors with 30 FPS or higher on GPU V100.
