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
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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
DPNet: Dual-Path Network for Real-time Object Detection with Lightweight Attention
huiminshii/dpnet
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The recent advances of compressing high-accuracy convolution neural networks (CNNs) have witnessed remarkable progress for real-time object detection.
SuperYOLO: Super Resolution Assisted Object Detection in Multimodal Remote Sensing Imagery
Furthermore, we design a simple and flexible SR branch to learn HR feature representations that can discriminate small objects from vast backgrounds with low-resolution (LR) input, thus further improving the detection accuracy.
Real Time Object Detection System with YOLO and CNN Models: A Review
The field of artificial intelligence is built on object detection techniques.
YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors
pjreddie/darknet
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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.
Vision Transformer Adapter for Dense Predictions
This work investigates a simple yet powerful dense prediction task adapter for Vision Transformer (ViT).
PP-YOLOE: An evolved version of YOLO
PaddlePaddle/PaddleDetection
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In this report, we present PP-YOLOE, an industrial state-of-the-art object detector with high performance and friendly deployment.
Real-time Object Detection for Streaming Perception
yancie-yjr/StreamYOLO
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CVPR 2022
In this paper, instead of searching trade-offs between accuracy and speed like previous works, we point out that endowing real-time models with the ability to predict the future is the key to dealing with this problem.
DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection
IDEA-Research/Grounded-Segment-Anything
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Compared to other models on the leaderboard, DINO significantly reduces its model size and pre-training data size while achieving better results.
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.
