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
Real-time Transformer-based Open-Vocabulary Detection with Efficient Fusion Head
om-ai-lab/OmDet
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End-to-end transformer-based detectors (DETRs) have shown exceptional performance in both closed-set and open-vocabulary object detection (OVD) tasks through the integration of language modalities.
YOLOv9: Learning What You Want to Learn Using Programmable Gradient Information
It can be used to obtain complete information, so that train-from-scratch models can achieve better results than state-of-the-art models pre-trained using large datasets, the comparison results are shown in Figure 1.
MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO
shubha07m/On-device-computer-vision-experiments-with-IoT
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Low-light conditions and occluded scenarios impede object detection in real-world Internet of Things (IoT) applications like autonomous vehicles and security systems.
Domain Adaptable Fine-Tune Distillation Framework For Advancing Farm Surveillance
The Fine-Tune Distillation framework aims to be adaptable to specific use cases, enabling the transfer of knowledge from the large models to the small models, making it suitable for domain-specific applications.
HIC-YOLOv5: Improved YOLOv5 For Small Object Detection
Small object detection has been a challenging problem in the field of object detection.
BGF-YOLO: Enhanced YOLOv8 with Multiscale Attentional Feature Fusion for Brain Tumor Detection
You Only Look Once (YOLO)-based object detectors have shown remarkable accuracy for automated brain tumor detection.
Gold-YOLO: Efficient Object Detector via Gather-and-Distribute Mechanism
huawei-noah/Efficient-Computing
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NeurIPS 2023
In the past years, YOLO-series models have emerged as the leading approaches in the area of real-time object detection.
YOLO-MS: Rethinking Multi-Scale Representation Learning for Real-time Object Detection
We aim at providing the object detection community with an efficient and performant object detector, termed YOLO-MS.
YUDO: YOLO for Uniform Directed Object Detection
This paper presents an efficient way of detecting directed objects by predicting their center coordinates and direction angle.
RCS-YOLO: A Fast and High-Accuracy Object Detector for Brain Tumor Detection
With an excellent balance between speed and accuracy, cutting-edge YOLO frameworks have become one of the most efficient algorithms for object detection.
