Using high-performance deep learning platform to accelerate object detection
Sergei Stepanenko,Pavel Yakimov,Photonics" Ras, Molodogvardejskaya street , Samara, Russia, +2 more
- 01 Jan 2019
TL;DR: This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed.
read more
Abstract: Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Investigations of Object Detection in Images/Videos Using Various Deep Learning Techniques and Embedded Platforms—A Comprehensive Review
TL;DR: This paper shows a detailed survey on recent advancements and achievements in object detection using various deep learning techniques, and identifies promising future directions.
126
A Practical Hybrid IoT Architecture with Deep Learning Technique for Healthcare and Security Applications
Viet Q. Vu,Minh-Quang Tran,Mohammed Amer,Mahesh Khatiwada,Sherif S. M. Ghoneim,Mahmoud Elsisi +5 more
TL;DR: Wang et al. as discussed by the authors introduced an Internet of Things (IoT) architecture based on a developed deep learning algorithm named You Only Look Once (YOLO) to keep society healthy, and secured, and collect data for future research.
Development of a cloud platform for gathering, storing and analysis of video data
Sergei Stepanenko,Pavel Yakimov +1 more
- 26 May 2020
TL;DR: An implementation of a platform which aggregates video from various sources and processes it with use of different services which are based on artificial neural networks and machine learning methods is presented.
1
Optimization of Deep Neural Network Models Based on JTRT Technique
Zihao Nie,Jian Qu +1 more
- 10 Nov 2022
TL;DR: In this article , the authors proposed to use the JTRT technique to accelerate the inference of deep neural network models, which can compress various network models and perform accelerated inference using semi-precision techniques to achieve optimization acceleration.
Optimization of Deep Neural Network Models Based on JTRT Technique
10 Nov 2022
TL;DR: In this paper , the authors proposed to use the JTRT technique to accelerate the inference of deep neural network models, which can compress various network models and perform accelerated inference using semi-precision techniques to achieve optimization acceleration.
References
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
You Only Look Once: Unified, Real-Time Object Detection
Joseph Redmon,Santosh K. Divvala,Ross Girshick,Ali Farhadi +3 more
- 27 Jun 2016
TL;DR: Compared to state-of-the-art detection systems, YOLO makes more localization errors but is less likely to predict false positives on background, and outperforms other detection methods, including DPM and R-CNN, when generalizing from natural images to other domains like artwork.
SSD: Single Shot MultiBox Detector
Wei Liu,Dragomir Anguelov,Dumitru Erhan,Christian Szegedy,Scott Reed,Cheng-Yang Fu,Alexander C. Berg +6 more
- 08 Oct 2016
TL;DR: The approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location, which makes SSD easy to train and straightforward to integrate into systems that require a detection component.
•Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
25.3K
YOLO9000: Better, Faster, Stronger
Joseph Redmon,Ali Farhadi +1 more
- 21 Jul 2017
TL;DR: YOLO9000 as discussed by the authors is a state-of-the-art real-time object detection system that can detect over 9000 object categories in real time using a novel multi-scale training method, offering an easy tradeoff between speed and accuracy.