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
Latest papers
CST-YOLO: A Novel Method for Blood Cell Detection Based on Improved YOLOv7 and CNN-Swin Transformer
Blood cell detection is a typical small-scale object detection problem in computer vision.
Mining Negative Temporal Contexts For False Positive Suppression In Real-Time Ultrasound Lesion Detection
To address this issue, we propose to extract contexts from previous frames, including NTC, with the guidance of inverse optical flow.
DETRs Beat YOLOs on Real-time Object Detection
Our RT-DETR-R50 / R101 achieves 53. 1% / 54. 3% AP on COCO and 108 / 74 FPS on T4 GPU, outperforming previously advanced YOLOs in both speed and accuracy.
Resolution Enhancement Processing on Low Quality Images Using Swin Transformer Based on Interval Dense Connection Strategy
We apply this strategy to SwinIR and present a new model, which named SwinOIR (Object Image Restoration Using Swin Transformer).
Help the Blind See: Assistance for the Visually Impaired through Augmented Acoustic Simulation
An estimated 253 million people have visual impairments.
YOLOv6 v3.0: A Full-Scale Reloading
PaddlePaddle/PaddleDetection
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For a glimpse of performance, our YOLOv6-N hits 37. 5% AP on the COCO dataset at a throughput of 1187 FPS tested with an NVIDIA Tesla T4 GPU.
DaDe: Delay-adaptive Detector for Streaming Perception
danjos95/dade
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Recognizing the surrounding environment at low latency is critical in autonomous driving.
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.
DAMO-YOLO : A Report on Real-Time Object Detection Design
Based on these new techs, we build a suite of models at various scales to meet the needs of different scenarios.
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.
