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 )
Libraries
Use these libraries to find Real-Time Object Detection models and implementationsDatasets
Latest papers with no code
Object-centric Cross-modal Feature Distillation for Event-based Object Detection
In this paper, we develop a novel knowledge distillation approach to shrink the performance gap between these two modalities.
Ultra-Efficient On-Device Object Detection on AI-Integrated Smart Glasses with TinyissimoYOLO
To this goal, we designed a smart glasses prototype as a research platform featuring two microcontrollers, including a novel milliwatt-power RISC-V parallel processor with a hardware accelerator for visual AI, and a Bluetooth low-power module for communication.
DEYOv3: DETR with YOLO for Real-time Object Detection
Due to this training method, the object detector does not need the additional dataset (ImageNet) to train the backbone, which makes the design of the backbone more flexible and dramatically reduces the training cost of the detector, which is helpful for the practical application of the object detector.
DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
In recent years, computer-aided diagnosis systems have shown great potential in assisting radiologists with accurate and efficient medical image analysis.
Leveraging the Edge and Cloud for V2X-Based Real-Time Object Detection in Autonomous Driving
An outstanding challenge in real-time perception for autonomous driving lies in finding the best trade-off between detection quality and latency.
Q-YOLO: Efficient Inference for Real-time Object Detection
Real-time object detection plays a vital role in various computer vision applications.
Real-Time Onboard Object Detection for Augmented Reality: Enhancing Head-Mounted Display with YOLOv8
This paper introduces a software architecture for real-time object detection using machine learning (ML) in an augmented reality (AR) environment.
Real-time Object Detection: YOLOv1 Re-Implementation in PyTorch
Real-time object detection is a crucial problem to solve when in comes to computer vision systems that needs to make appropriate decision based on detection in a timely manner.
Computer Vision for Construction Progress Monitoring: A Real-Time Object Detection Approach
Construction progress monitoring (CPM) is essential for effective project management, ensuring on-time and on-budget delivery.
TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers
The proposed quantized network architecture with 422k parameters, enables real-time object detection on embedded microcontrollers, and it has been evaluated to exploit CNN accelerators.